{"id":4702,"date":"2023-02-27T10:11:05","date_gmt":"2023-02-27T02:11:05","guid":{"rendered":"http:\/\/192.168.10.115\/index.php\/2023\/02\/27\/4702\/"},"modified":"2023-02-27T10:36:07","modified_gmt":"2023-02-27T02:36:07","slug":"%e7%bc%96%e8%be%91%e4%ba%8e-2023-02-26-2207%e3%83%bbip-%e5%b1%9e%e5%9c%b0%e6%b5%99%e6%b1%9f%e8%87%aa%e5%8a%a8%e9%a9%be%e9%a9%b6%e4%b8%ad%e7%9a%84bev%e6%84%9f%e7%9f%a5%e4%b8%8e%e5%bb%ba%e5%9b%be","status":"publish","type":"post","link":"http:\/\/222.128.65.89:18086\/index.php\/2023\/02\/27\/4702\/","title":{"rendered":"2023-02-26\u81ea\u52a8\u9a7e\u9a76\u4e2d\u7684BEV\u611f\u77e5\u4e0e\u5efa\u56fe\u5c0f\u8bb0"},"content":{"rendered":"

\u7f16\u8f91\u4e8e 2023-02-26 22:07\u30fbIP \u5c5e\u5730\u6d59\u6c5f\u200b<\/p>\n

\u81ea\u52a8\u9a7e\u9a76\uff1aBEV\u611f\u77e5<\/p>\n

\u80cc\u666f\u4ecb\u7ecd<\/p>\n

\u65e9\u671f\u7684\u81ea\u52a8\u9a7e\u9a76\u611f\u77e5\u901a\u5e38\u662f\u5728\u56fe\u50cf\u7a7a\u95f4\u8fdb\u884c\uff0c\u4f46\u662f\u968f\u7740\u6444\u50cf\u5934\u7684\u589e\u591a\uff0c\u591a\u76f8\u673a\u878d\u5408\u548c\u4e0b\u6e38\u89c4\u63a7\u7684\u5e94\u7528\u9700\u6c42\uff0c\u4f7f\u5f97\u57fa\u4e8eBEV\u7a7a\u95f4\u7684\u611f\u77e5\u7b97\u6cd5\u65e5\u8d8b\u6d41\u884c\u3002BEV\u611f\u77e5\u9996\u5148\u8981\u89e3\u51b3\u7684\u95ee\u9898\u662f\u56fe\u50cf\u7a7a\u95f4\u5230BEV\u7a7a\u95f4\u7684\u53d8\u6362\uff0c\u4ece\u76f8\u673a\u89c6\u56fe\u5230\u9e1f\u77b0\u56fe\u89c6\u89d2\u8f6c\u6362\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u7247\u4e0a\u6bcf\u4e2a\u50cf\u7d20\u70b9\u7684\u6df1\u5ea6\u4fe1\u606f\uff0c\u65e0\u6cd5\u7b80\u5355\u5730\u5229\u7528\u51e0\u4f55\u6295\u5f71\u5c06\u56fe\u50cf\u6295\u56de\u5230\u9e1f\u77b0\u89c6\u89d2\u3002\u5e38\u7528\u7684PV2BEV\u65b9\u6848\uff1a<\/p>\n

depth\uff1a\u4f7f\u7528\u4f30\u8ba1\u6df1\u5ea6\u8d4b\u503cRGB\u50cf\u7d20\u5b8c\u6210\u56fe\u50cf2d->3d\u53d8\u6362\uff0c\u7136\u540e\u53d6XY\u7ef4\u5ea6\u751f\u6210BEV\u7279\u5f81<\/p>\n

VED[1]\uff1a\u7528\u5177\u6709 MLP bottleneck\u5c42\u7684\u53d8\u5206\u7f16\u7801\u5668-\u89e3\u7801\u5668\u6765\u5b9e\u73b0 PV-to-BEV \u8f6c\u6362<\/p>\n

VPN[2]\uff1a\u7528\u5168\u8fde\u63a5\u7f51\u7edc\u6765\u8ba9network\u81ea\u5df1\u5b66\u4e60\u5982\u4f55\u8fdb\u884c\u89c6\u89d2\u7684\u53d8\u6362<\/p>\n

PON[3]\uff1a\u7528\u7279\u5f81\u91d1\u5b57\u5854\u63d0\u53d6\u591a\u5206\u8fa8\u7387\u7279\u5f81\uff0c\u7136\u540e\u6cbf\u9ad8\u5ea6\u8f74\u6298\u53e0\u7279\u5f81\uff0c\u5e76\u4f7f\u7528MLP\u6cbf\u6df1\u5ea6\u8f74\u5c55\u5f00\u5b9e\u73b0\u89c6\u56fe\u8f6c\u6362<\/p>\n

IPM[4]\uff1a\u7528\u5730\u9762\u662f\u5e73\u9762\u7684\u5047\u8bbe\u8fdb\u884c\u9006\u900f\u89c6\u53d8\u6362\u3002\u7531\u4e8e\u5730\u9762\u5b58\u5728\u8d77\u4f0f\uff0c\u6240\u4ee5\u8be5\u5047\u8bbe\u5728\u90e8\u5206\u573a\u666f\u4e0d\u6210\u7acb\u3002<\/p>\n

GKT[5]\uff1a\u6bcf\u4e2aBEV query\u901a\u8fc7\u76f8\u673a\u5185\u5916\u53c2\u83b7\u53d6\u5404\u4e2a\u89c6\u89d2\u5bf9\u5e94\u56fe\u50cf\u5750\u6807(u,v)\uff0c\u63d0\u53d6\u9644\u8fd1\u7684Kh\u00d7Kw\u6838\u533a\u57df\u7684\u7279\u5f81\u3002<\/p>\n

MLP Fully Connect Layer\u548cCross Attention\u7684\u663e\u8457\u5dee\u522b\u5728\u4e8e\u4f5c\u7528\u4e8e\u8f93\u5165\u91cfX\u7684\u7cfb\u6570W\uff1a**\u5168\u8054\u63a5\u5c42\u7684W\uff0c\u4e00\u65e6\u8bad\u7ec3\u7ed3\u675f\u540e\u5728Inference\u9636\u6bb5\u662f\u56fa\u5b9a\u4e0d\u53d8\u7684\uff1b\u800cCross Attention\u7684Transformer\u7684\u7cfb\u6570W\uff0c\u662f\u8f93\u5165\u91cfX\u548c\u7d22\u5f15\u91cf varPhi\u7684\u51fd\u6570\uff0c\u5728Inference\u9636\u6bb5\u4f1a\u6839\u636e\u8f93\u5165\u91cfX\u548c\u7d22\u5f15\u91cf\u7684\u4e0d\u540c\u53d1\u751f\u6539\u53d8\u3002\u4ece\u8fd9\u4e2a\u89d2\u5ea6\u6765\u8bb2\uff0c\u4f7f\u7528Cross Attention\u6765\u8fdb\u884c\u7a7a\u95f4\u53d8\u6362\u53ef\u80fd\u4f7f\u6a21\u578b\u83b7\u5f97\u66f4\u5f3a\u7684\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

PV2BEV\u8f6c\u6362\u516c\u5f0f<\/p>\n

\u5982\u4e0a\u8ff0\u7b49\u5f0f\u6240\u793a\uff0cPV\u548cBEV\u7684\u5750\u6807\u662f\u53ef\u4ee5\u76f8\u4e92\u8f6c\u6362\u7684\uff0c\u4f46\u662f\u76ee\u524d\u6df1\u5ea6z\u548cBEV\u5750\u6807\u90fd\u662f\u672a\u77e5\u7684\u3002\u6211\u4eec\u5982\u679c\u60f3\u83b7\u53d6\u5230BEV\u4e0b\u7684\u7279\u5f81\u5750\u6807\u67092\u79cd\u65b9\u5f0f\uff1a\u65b9\u5f0f1(2d->3D)\u662f\u4ecePV\u4f30\u8ba1\u6df1\u5ea6Z\uff0c\u6709\u4e86\u6df1\u5ea6\u7136\u540e\u53d8\u6362BEV\u5750\u6807\uff1b\u65b9\u5f0f2(3D->2D)\u662f\u76f4\u63a5\u4f30\u8ba1BEV\u5750\u6807\uff0c\u6709\u4e86BEV\u5750\u6807\u81ea\u7136\u77e5\u9053\u6df1\u5ea6\uff0c\u4ece\u800c\u5173\u8054\u5230PV\u5750\u6807\u3002<\/p>\n

\u8fd9\u91cc\u4e3b\u8981\u6c47\u603b\u4ecb\u7ecd\u4ee5\u4e0b2\u7c7b\uff1a<\/p>\n

\u901a\u8fc7\u663e\u5f0f\u6df1\u5ea6\u4f30\u8ba1\u540e\uff0c\u91c7\u7528\u76f8\u673a\u5185\u3001\u5916\u53c2\u8fdb\u884c\u663e\u5f0f\u7684PV2BEV\u89c6\u89d2\u53d8\u6362\uff0c\u4ee3\u8868\u662f\uff1aLLS\u3002<\/p>\n

\u901a\u8fc7self attention\u83b7\u53d6BEV query\uff0c\u7136\u540e\u5bf9\u56fe\u50cf\u7279\u5f81\u505across attention\uff0c\u5b9e\u73b0PV2BEV\u83b7\u53d6BEV\u7279\u5f81\u3002<\/p>\n

\u65e9\u671f\u7684attention\u4e0d\u9700\u8981\u4f9d\u8d56\u5185\u5916\u53c2\uff0c\u4f46\u662f\u8fd9\u79cd\u65b9\u5f0f\u6548\u7387\u592a\u4f4e\u3002\u9274\u4e8e\u76f8\u673a\u7684\u6807\u5b9a\u8bef\u5dee\u6240\u5bfc\u81f4\u7684\u7ed3\u679c\u504f\u79fb\u662f\u53ef\u4f30\u91cf\u548c\u9632\u8303\u7684\uff0c\u6240\u4ee5\u540e\u7eed\u57fa\u672c\u90fd\u4f1a\u4f7f\u7528\u76f8\u673a\u5185\u5916\u53c2\u6765\u52a0\u901f\u8ba1\u7b97\u3002\u4e0b\u9762\u662f2021-2022\u5e74BEV\u53d1\u5c55\u7684\u6d41\u7a0b\u56fe\uff1a<\/p>\n

<\/p>\n

BEV\u53d1\u5c55\u6d41\u7a0b\u56fe<\/p>\n

LSS\u67b6\u6784[6]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb[7]LLS\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf<\/p>\n

Lift:\u63d0\u53d6pv\u56fe\u50cf\u7279\u5f81,\u5728\u7279\u5f81\u56fe\u76f8\u7d20\u5c04\u7ebf\u4e0a\u53d6D\u4e2a\u6df1\u5ea6\u503c\u5f62\u6210\u89c6\u9525\u70b9\u4e91,\u5bf9\u6bcf\u4e2a\u89c6\u9525\u70b9\u4f30\u8ba1C\u4e2a\u7279\u5f81\u7684\u5206\u5e03\u6982\u7387.<\/p>\n

Lift\u8fc7\u7a0b<\/p>\n

Splat:\u7528\u5185\u5916\u53c2\u628a\u6240\u6709\u89c6\u9525\u70b9\u6295\u5f71\u5230BEV\u7f51\u683c\u4e2d,\u5bf9\u6bcf\u4e2a\u6805\u683c\u4e2d\u591a\u4e2a\u89c6\u9525\u70b9\u8fdb\u884csum-pooling\u5f62\u6210BEV\u7279\u5f81\u56fe\uff1b<\/p>\n

Shoot:\u7528task head\u5904\u7406BEV\u7279\u5f81\u56fe\uff0c\u5b9e\u73b0\u7aef\u5230\u7aef\u7684\u8fd0\u52a8\u89c4\u5212\u3002(BEV\u6700\u521d\u5c31\u662f\u4e3a\u4e86\u89c4\u63a7\u3002)<\/p>\n

<\/p>\n

BEV\u7279\u5f81\u56fe\u7528\u4e8emotion planning<\/p>\n

BEVDet[8]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb<\/p>\n

BEVDet\u6846\u67b6<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf(\u6539\u8fdbLSS\u67b6\u6784)<\/p>\n

\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\uff1a\u5bf9\u8f93\u5165\u56fe\u50cf\u505a\u7ffb\u8f6c\u3001\u88c1\u526a\u3001\u65cb\u8f6c\u7b49\u6570\u636e\u589e\u5f3a\uff0c\u7136\u540e\u7528resnet\u63d0\u53d6\u7279\u5f81\u3002<\/p>\n

\u89c6\u89d2\u8f6c\u6362\u5668\uff1a\u5bf9\u8f93\u5165\u7684\u7279\u5f81\u56fe\u6267\u884c\u6570\u636e\u589e\u5f3a\u9006\u64cd\u4f5c\uff0c\u4ece\u800c\u4fdd\u6301\u56fe\u50cf\u7279\u5f81\u5728BEV\u7a7a\u95f4\u4e0a\u7684\u4e00\u81f4\u6027<\/p>\n

BEV\u7279\u5f81\u63d0\u53d6\uff1a\u91c7\u7528resnet\uff0c\u63d0\u53d6\u7684BEV\u7279\u5f81\u4e5f\u505a\u6570\u636e\u589e\u5f3a\uff0c\u540c\u65f6\u5bf9\u4e0d\u540c\u7c7b\u522b\u7684\u7269\u4f53\u5728\u76f8\u540c\u7684\u5c3a\u5ea6\u4e0b\u505aNMS\u64cd\u4f5c\u3002<\/p>\n

\u4e0d\u540c\u7c7b\u522b\u3001\u540c\u5c3a\u5ea6\u7684IOU<\/p>\n

BEVDet4D<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb[9]<\/p>\n

BEV4D\u5bf9\u9f50\u878d\u5408\u8fc7\u7a0b<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf(\u6539\u8fdb\u57fa\u4e8eLSS\u67b6\u6784\u7684BEVDet)<\/p>\n

\u878d\u5408\u65f6\u5e8f\u7279\u5f81\uff1a\u5c06\u5386\u53f2\u5e27BEV\u7279\u5f81\u5750\u6807\uff0c\u7528\u4f4d\u59ff\u5deeT\u53d8\u6362\u5230\u5f53\u524d\u5e27BEV\u4e0b\uff0c\u4e0e\u5f53\u524d\u5e27\u76f8\u540c\u4f4d\u7f6eBEV\u7279\u5f81concate\u3002<\/p>\n

BEVDepth[10]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb[11]<\/p>\n

BEVDepth\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf(\u6539\u8fdbLLS\u67b6\u6784)<\/p>\n

Depth \u76d1\u7763\uff1abackbone\u63d0\u53d6\u7279\u5f81\u8f93\u51fa\u7ed9DepthNet\u4f30\u8ba1\u6bcf\u4e2a\u7279\u5f81\u70b9\u7684\u6df1\u5ea6d\uff0c\u76f8\u6bd4\u4e8eLLS\u7684\u65e0\u6df1\u5ea6\u76d1\u7763\u8bad\u7ec3\uff0c DepthNet\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u663e\u6027\u7684\u91c7\u7528\u70b9\u4e91\u6df1\u5ea6\u505a\u76d1\u7763\uff1b<\/p>\n

Depth\u76d1\u7763\u7684\u4f5c\u7528<\/p>\n

Depth Correction\uff1a\u4e3a\u4e86\u89e3\u51b3\u76f8\u673a\u5185\u5916\u53c2\u4e0d\u51c6\u5bfc\u81f4\u7684\uff0c\u6df1\u5ea6\u76d1\u7763gt\u4e0e\u7279\u5f81\u9519\u5339\u914d\uff0c\u5806\u53e03\u4e2aResidual Blocks\uff0c\u964d\u4f4e\u7279\u5f81\u5c3a\u5bf8\u6765\u589e\u5927\u611f\u53d7\u91ce\uff0c\u7136\u540e\u5728\u5b83\u7684\u540e\u9762\u518d\u63a5\u4e0aDeformable Conv\uff08DCN\uff09\u6a21\u5757\uff0c\u901a\u8fc7\u589e\u5927\u611f\u53d7\u91ce\uff0c\u65e0\u6cd5\u5bf9\u9f50\u7684\u6df1\u5ea6\u771f\u503c\u4e5f\u80fd\u53c2\u4e0e\u51c6\u786e\u5bf9\u9f50\u6b63\u786e\u4f4d\u7f6e\u4e0a\u7279\u5f81\u7684\u8ba1\u7b97\u3002<\/p>\n

Depth Correction<\/p>\n

Camera-aware Depth Prediction:\u5148\u7528MLP\u628a\u76f8\u673a\u5185\u53c2\u6269\u5c55\u5230feature\u7684\u7ef4\u5ea6\uff0c\u7136\u540e\u7528\u5b83\u4eec\u5bf9\u56fe\u50cf\u7279\u5f81\u8fdb\u884c\u91cd\u65b0\u52a0\u6743(re-weight)\uff0c\u8fd9\u4e00\u6b65\u4f7f\u7528Squeeze-and-Excitation\u6a21\u5757\uff0c\u6700\u540e\u628a\u76f8\u673a\u5916\u53c2\u548c\u5185\u53c2\u4e32\u8054\u8ba9DepthNet\u611f\u77e5\u5230F^{2d}\u5728\u81ea\u8f66\u5750\u6807\u7cfb\u4e2d\u7684\u7a7a\u95f4\u4f4d\u7f6e.<\/p>\n

Efficient Voxel Pooling\uff1a\u4e3a\u89c6\u9525\u7684\u6bcf\u4e2a\u7279\u5f81\u70b9\u5206\u914d\u4e00\u4e2aCUDA thread\uff0c\u5b83\u7684\u4f5c\u7528\u662f\u628a\u7279\u5f81\u503c\u52a0\u5230\u76f8\u5e94\u7684BEV\u7f51\u683c\u4e2d\uff0c\u5229\u7528\u4e86GPU\u8ba1\u7b97\u7684\u5e76\u884c\u5316\u5b8c\u6210\u7a20\u5bc6\u7279\u5f81\u70b9\u7684\u6c60\u5316\u8fc7\u7a0b\u3002<\/p>\n

Multi-frame Fusion\uff1a\u5c06T-1\u65f6\u523b\u7684\u89c6\u9525\u70b9\u53d8\u6362\u5bf9\u9f50\u5230T\u65f6\u523b\uff0c\u7136\u540evoxel pooling\u751f\u6210BEV\uff0c\u5e76\u5408\u5e76\u6210\u65b0BEV\u7279\u5f81<\/p>\n

\u591a\u5e27\u878d\u5408<\/p>\n

3D Detection head\uff1a\u628aLSS\u4e2d\u7684\u5206\u5272head\u66ff\u6362\u6210\u4e863D\u76ee\u6807\u68c0\u6d4b\u7684CenterPoint Head\uff0c\u9884\u6d4b\u76ee\u6807class\u30013D box\u7684offset\u4ee5\u53ca\u5176\u5b83\u5c5e\u6027\u3002<\/p>\n

BEVFusion(Mit)[12]<\/p>\n

\u8d44\u6599\uff1a\u4ee3\u7801 \u8bba\u6587 \u89e3\u8bfb<\/p>\n

BEVFusion(Mit)\u6548\u679c<\/p>\n

BEVFusion(Mit)\u6846\u67b6<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a(\u6539\u8fdbLSS\u67b6\u6784)<\/p>\n

\u56fe\u50cf\uff1a\u91c7\u7528\u52a0\u901f\u7684LLS\uff0c\u4f1a\u9884\u5148\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u70b9\u76843D\u5750\u6807\u548cBEV\u7f51\u683c\u7d22\u5f15\uff0c\u540c\u65f6\u5b9e\u73b0\u4e86\u4e00\u4e2a\u76f4\u63a5\u5728BEV\u7f51\u683c\u4e0a\u5e76\u884c\u5316\u7684\u4e13\u7528GPU\u5185\u6838\uff1a\u4e3a\u6bcf\u4e00\u4e2aGPU\u7ebf\u7a0b\u8ba1\u7b97\u95f4\u9694\u548c\u5e76\u8fd4\u56de\u7ed3\u679c\u3002<\/p>\n

LLS\u52a0\u901f<\/p>\n

\u70b9\u4e91\uff1a\u5bf9\u6fc0\u5149\u7279\u5f81\u8fdb\u884cFlatten\u83b7\u53d6BEV\u7279\u5f81\u3002<\/p>\n

\u878d\u5408\uff1a\u7531\u4e8e\u89c6\u89c9\u7684\u6df1\u5ea6\u8f6c\u6362\u5668\u7684\u6df1\u5ea6\u4e0d\u51c6\u786e\uff0c\u7528\u5e26\u6709\u6b8b\u5dee\u7684\u5377\u79ef\u53bb\u5904\u7406\u878d\u5408\u7684BEV\u7279\u5f81\uff0c\u6765\u8865\u507f\u672a\u5bf9\u9f50\u7279\u5f81\u3002<\/p>\n

BEVFusion(\u5317\u5927)[13]<\/p>\n

\u8d44\u6599\uff1a\u4ee3\u7801 \u8bba\u6587 \u89e3\u8bfb<\/p>\n

BEVFusion\u6846\u67b6<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a(\u6539\u8fdbLSS\u67b6\u6784)<\/p>\n

\u56fe\u50cf\uff1a\u6539\u8fdb\u4e86LSS\uff0c\u5c062Dbackbone\u66ff\u6362\u4e3aDual-Swin-Tiny\uff0c\u63a5FPN\u63d0\u53d6\u591a\u5c3a\u5ea6\u7279\u5f81\uff0cADP\u6765\u5bf9\u9f50\u591a\u5c3a\u5ea6\u7279\u5f81\u3002\u901a\u8fc7\u56fe\u50cf\u7684\u6df1\u5ea6\u4f30\u8ba1\u8fdb\u884c\u7279\u5f812d->3d\u7684\u53d8\u6362\u83b7\u53d6\u7279\u5f81Y*Y*Z*C\uff0c\u7136\u540e\u7528S2C\u5c064D\u7279\u5f81\u964d\u7ef4\u4e3a3D\u7279\u5f81Y*Y*(ZC)\u3002<\/p>\n

\u70b9\u4e91\uff1a\u91c7\u7528\u5e38\u7528\u7684PointPillars\u3001CenterPoint\u3001TransFusion\u6765\u964d\u4f4eZ\u7ef4\u5ea6\u751f\u6210BEV\u7279\u5f81\u3002<\/p>\n

\u878d\u5408\uff1a\u901a\u8fc7\u4e00\u4e2a\u901a\u9053\u6ce8\u610f\u6a21\u5757\u6765\u9009\u62e9\u91cd\u8981\u7684\u878d\u5408\u7279\u5f81\u3002\u505a\u6cd5\u5982\u4e0b\uff1a\u901a\u8fc73*3\u5377\u79ef\u5bf9concate\u7684lidar\u548ccamera\u7279\u5f81\u7f29\u51cf\u901a\u9053\u4e0elidar\u4fdd\u6301\u4e00\u81f4\uff0c\u7136\u540e\u901a\u8fc7\u5168\u5c40\u5e73\u5747\u6c60\u5316\u548c1*1\u5377\u79ef\u5904\u7406\u540e\u9001\u7ed9sigmoid,\u6700\u540e\u4e0e\u7279\u5f81\u505achannel-wise Multiply.<\/p>\n

\u7279\u5f81\u901a\u9053\u52a8\u6001\u878d\u5408\u6a21\u5757<\/p>\n

\u68c0\u6d4b\u5934\uff1a\u91c7\u7528\u4e86\u6d41\u884c\u7684anchor-base\u3001anchor-free\u3001transform head,\u9a8c\u8bc1\u7279\u5f81\u63d0\u53d6\u6846\u67b6\u5bf9\u4e8e\u68c0\u6d4b\u5934\u7684\u6cdb\u5316\u6027\u3002<\/p>\n

DETR3D[14]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb<\/p>\n

DETR3D\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf\uff08\u57fa\u4e8etransformer\u6846\u67b6\uff09<\/p>\n

Encoder\uff1a\u7528ResNet+4\u5c42FPN\u63d0\u53d6\u7279\u5f81\u3002<\/p>\n

Decoder\uff1a\u5bf9\u9884\u8bbe\u7684object query\u7528\u4e00\u4e2a\u5168\u8fde\u63a5\u7f51\u7edc\u9884\u6d4b\u5728BEV\u7a7a\u95f4\u4e2d\u76843D reference point(x,y,z,)\uff0c\u7ecf\u8fc7sigmoid\u5f52\u4e00\u5316\u540e\u8868\u793a\u5728\u7a7a\u95f4\u4e2d\u7684\u76f8\u5bf9\u4f4d\u7f6e\uff1b\u75286\u5c42transformer decoder layer\uff0c\u5728\u6bcf\u5c42layer\u4e4b\u4e2d\uff0c\u6240\u6709\u7684object query\u4e4b\u95f4\u505aself-attention\uff0c\u518d\u548c\u56fe\u50cf\u7279\u5f81\u4e4b\u95f4\u505across-attention\uff1a\u5c06\u6bcf\u4e2aquery\u5bf9\u5e94\u76843D reference point\u901a\u8fc7\u76f8\u673a\u7684\u5185\u53c2\u5916\u53c2\u6295\u5f71\u5230\u56fe\u7247\u5750\u6807\uff0c\u5229\u7528\u7ebf\u6027\u63d2\u503c\u6765\u91c7\u6837\u5bf9\u5e94\u7684multi-scale image features\uff0c\u4e4b\u540e\u518d\u7528sampled image features\u53bb\u66f4\u65b0object queries\u3002\u7ecf\u8fc7attention\u66f4\u65b0\u540e\u7684object query\u901a\u8fc7\u4e24\u4e2aMLP\u7f51\u7edc\u6765\u5206\u522b\u9884\u6d4b\u5bf9\u5e94\u7269\u4f53\u7684class\u548cbounding box\u7684\u53c2\u6570\u3002\u4e3a\u4e86\u8ba9\u7f51\u7edc\u66f4\u597d\u7684\u5b66\u4e60\uff0c\u6211\u4eec\u6bcf\u6b21\u90fd\u9884\u6d4bbounding box\u7684\u4e2d\u5fc3\u5750\u6807\u76f8\u5bf9\u4e8ereference points\u7684offset(Delta x, Delta y, Delta z)\u6765\u66f4\u65b0reference points\u7684\u5750\u6807\u3002\u6bcf\u5c42\u66f4\u65b0\u7684object queries\u548creference points\u4f5c\u4e3a\u4e0b\u4e00\u5c42decoder layer\u7684\u8f93\u5165\uff0c\u518d\u6b21\u8fdb\u884c\u8ba1\u7b97\u66f4\u65b0\uff0c\u603b\u5171\u8fed\u4ee36\u6b21\u3002<\/p>\n

Loss\uff1a\u6240\u6709object queries\u9884\u6d4b\u51fa\u6765\u7684\u68c0\u6d4b\u6846\u548c\u6240\u6709\u7684ground-truth bounding box\u4e4b\u95f4\u5229\u7528\u5308\u7259\u5229\u7b97\u6cd5\u8fdb\u884c\u4e8c\u5206\u56fe\u5339\u914d\uff0c\u627e\u5230\u4f7f\u5f97loss\u6700\u5c0f\u7684\u6700\u4f18\u5339\u914d\uff0c\u5e76\u8ba1\u7b97classification focal loss\u548cL1 regression loss\u3002<\/p>\n

PETR[15]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb[16]<\/p>\n

PETR\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf(\u57fa\u4e8etransformer\u6846\u67b6)<\/p>\n

3D position embedding\uff1a\u83b7\u53d6\u89c6\u9525\u4f533d\u70b9\u4e91\u540e\uff0c\u72ec\u7acb\u7684\u8fdb\u884c\u4f4d\u7f6e\u7f16\u7801\uff0c\u7136\u540e\u4e0e2d\u7279\u5f81\u8fdb\u884cconcate\u3002\u628aDETR3D\u4e2dcross attention\u7684\u591a\u89c6\u89d2\u6295\u5f71\u8fc7\u7a0b\u7528\u4e8b\u5148\u8ba1\u7b97\u597d\u76843D positional encoding\u53d6\u4ee3,\u4e0equery\u76f8\u52a0\u540e\u4e5f\u53ef\u4ee5\u4e3a2D\u70b9\u63d0\u4f9b3D\u4fe1\u606f,\u56e0\u4e3a\u53ea\u8981\u5916\u53c2\u4e0d\u53d8,\u6bcf\u6b21\u6295\u5f71\u8fc7\u7a0b\u5176\u5b9e\u662f\u76f8\u540c\u7684,\u6ca1\u5fc5\u8981\u6bcf\u4e2aiteration\u90fd\u91cd\u590d\u8ba1\u7b97,\u5927\u5927\u63d0\u9ad8\u4e86\u6548\u7387\u3002[17]<\/p>\n

<\/p>\n

\u91c7\u75283D\u4f4d\u7f6e\u7f16\u7801\u4f18\u52bf3D\u4f4d\u7f6e\u7f16\u7801\u5668<\/p>\n

BEVFormer[18]<\/p>\n

\u4ee3\u7801 \u8bba\u6587 \u89e3\u8bfb[19]\u4f5c\u8005\u5f00\u8bb2[20]\u4e00\u6587\u8bfb\u61c2[21]<\/p>\n

BEVFormer\u6846\u67b6\u56fe<\/p>\n

\u610f\u4e49\uff1a\u5f15\u5165\u65f6\u5e8f\u4fe1\u606f\u4e4b\u540e\uff0c\u57fa\u4e8e\u7eaf\u89c6\u89c9\u7684\u6a21\u578b\u771f\u6b63\u80fd\u591f\u9884\u6d4b\u7269\u4f53\u7684\u79fb\u52a8\u901f\u5ea6\uff0c\u4e14\u65b9\u4fbf\u7684\u7528\u4e8e\u89c4\u63a7\u3002<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf\uff08\u57fa\u4e8etransformer\uff09<\/p>\n

2d backbone\uff1a\u4e24\u79cd\uff0c\u91c7\u7528\u6765\u81eaFCOS3D\u8bad\u7ec3\u5f97\u5230\u7684ResNet101-DCN\uff1b\u91c7\u7528\u6765\u81eaDD3D\u8bad\u7ec3\u5f97\u5230\u7684VoVnet-99\u3002<\/p>\n

3D query\u751f\u6210\uff1a\u6bcf\u4e2aBEV\u4f4d\u7f6e\u91c7\u6837\u4e864\u4e2a\u9ad8\u5ea6\u5f97\u52303D reference points(-5m\u52303m\uff0c\u6bcf2\u7c73\u91c7\u6837\u4e00\u4e2a\u70b9)<\/p>\n

spatial cross-attention\uff1a\u5c06BEV query\u8fdb\u884c\u53d8\u6362\u4e3axyz\u76843d\u70b9,\u901a\u8fc7\u76f8\u673a\u5185\u5916\u53c2\u83b7\u53d6\u591a\u89c6\u89d2pv\u5750\u6807\uff0c\u4ee5\u8fd9\u4e9buv\u70b9\u4f5c\u4e3a\u4e2d\u5fc3\u8fdb\u884c\u7279\u5f81\u91c7\u6837\uff0c\u52a0\u6743\u878d\u5408\u4e3aBEV\u7279\u5f81\uff1b<\/p>\n

SCA<\/p>\n

temporal self-attention\uff1a\u5148\u5c06t-1\u65f6\u523bBEV\u7279\u5f81\u7528ego-motion\u53d8\u6362\u5230t\u65f6\u523b\uff0c\u5728t\u65f6\u523b\u7684xy query\u9644\u8fd1\u91c7\u6837\u53d8\u6362\u540et-1\u65f6\u523b\u7684BEV\u7279\u5f81\u4e0et\u65f6\u523b\u7684BEV\u7279\u5f81\u805a\u5408\uff0c\u4e8c\u8005\u7684\u6743\u91cd\u901a\u8fc7attention weights\u6765\u5e73\u8861\u3002\u5982\u4e0a\u56fe\u6700\u53f3\u4fa7(b)(c)\u3002<\/p>\n

TSA<\/p>\n

SCA\u548cTSA\u8fc7\u7a0b\u91cd\u590d6\u6b21\uff0c\u786e\u4fdd\u65f6\u7a7a\u7279\u5f81\u80fd\u591f\u76f8\u4e92\u4fc3\u8fdb\uff0c\u8fdb\u884c\u66f4\u7cbe\u51c6\u7684\u7279\u5f81\u878d\u5408.<\/p>\n

PolarDETR[22]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb<\/p>\n

PolarDETR\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf\uff08\u57fa\u4e8etransformer\uff09<\/p>\n

\u5750\u6807\u7cfb\uff1abev\u53c2\u8003\u4ece\u7b1b\u5361\u5c14\u5750\u6807\u7cfb\u8f6c\u6362\u5230\u4e86\u6781\u5750\u6807\u7cfb.\u4f18\u70b9\uff1a\u4e24\u4e2a\u76ee\u6807\u8ddd\u81ea\u8f66\u8ddd\u79bb\u76f8\u7b49,\u5c31\u5c06\u88ab\u540c\u7b49\u5bf9\u5f85\u3002<\/p>\n

\u6781\u5750\u6807\u7cfb\u5bf9\u4e8e\u5468\u56f4\u7a7a\u95f4\u611f\u77e5\u8ddd\u79bb\u662f\u4e00\u81f4\u7684<\/p>\n

\u7279\u5f81\u878d\u5408\uff1a\u878d\u5408\u7684\u662f\u4ee3\u8868\u76ee\u6807\u7684object query,\u800c\u4e0d\u662f\u4ee3\u8868bev feature\u7684bev query.<\/p>\n

PETRv2[23]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb<\/p>\n

PETRv2\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a<\/p>\n

\u65f6\u5e8f\u878d\u5408\uff1a\u901a\u8fc7\u53d8\u6362\u524d\u5e8f\u5e27\u5916\u53c2\u7684\u65b9\u5f0f,\u5c06\u524d\u5e8f\u5e27\u7684\u76f8\u673a\u89c6\u89d2\u53d8\u6210\u5f53\u524d\u5e27\u7684\u865a\u62df\u89c6\u89d2\u3002<\/p>\n

BEVFormerV2[24]<\/p>\n

BEVFormerV2<\/p>\n

\u8d44\u6599\uff1a\u8bba\u6587 \u89e3\u8bfb[25]<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf<\/p>\n

perspective 3D detection head&Two-stage BEV Decoder<\/p>\n

image backbone:\u6ca1\u6709\u4f7f\u7528\u4efb\u4f55\u81ea\u52a8\u9a7e\u9a76\u6570\u636e\u96c6\u6216\u5176\u4ed6\u6df1\u5ea6\u4f30\u8ba1\u6570\u636e\u96c6\u8fdb\u884c\u9884\u8bad\u7ec3\u8fc7\u7684modern backbone.<\/p>\n

perspective 3D detection head:\u5728BEVFormer\u4e2d\u662f\u76d1\u7763BEV\u7279\u5f81\u8bad\u7ec3\u7684,\u73b0\u5728\u5c06\u771f\u503c\u76d1\u7763\u524d\u63a8\u5230BEV\u53d8\u6362\u5668\u4e4b\u524d,\u76f4\u63a5\u76d1\u7763backbone 2d\u7279\u5f81\u7684\u8bad\u7ec3\uff0c\u901a\u8fc7\u5728backbone\u4e0a\u6dfb\u52a0FCOS3DDD3D\u7b49\u900f\u89c6\u68c0\u6d4b\u5934\uff0c\u907f\u514dBEV\u53d8\u6362\u5bfc\u81f4\u7684\u5931\u771f\u4fc3\u8fdbbackbone\u9488\u5bf93D\u68c0\u6d4b\u4efb\u52a1\u7684\u9002\u5e94\u6027,\u751f\u6210\u7684proposals\u7ecf\u8fc7\u540e\u5904\u7406\u8fd8\u7528\u4e8e\u751f\u6210objectory query.<\/p>\n

spatial encoder:\u4e0eBEVFormer\u4fdd\u6301\u4e00\u81f4.<\/p>\n

temporal encoder:\u4e0eBEVDet4D\u76f8\u4f3c,\u540c\u65f6\u8f93\u5165\u591a\u5e27,\u505a\u597dalignment\u4ee5\u540econcate\u8d77\u6765,\u53ea\u662fBEVDet4D\u4e0d\u662f\u57fa\u4e8etransformer\u7684\u878d\u5408\u65b9\u5f0f,\u662f\u901a\u8fc7\u8f93\u5165bev encoder,\u4e5f\u5c31\u662fCNN\u7f51\u7edc\u6765\u8fdb\u884c\u878d\u5408,\u800cbevformer v2\u662f\u7ecf\u8fc7residual block(\u6b8b\u5dee\u8fde\u63a5\u5757),\u4e5f\u5c31\u662fresnet\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206,\u8fdb\u884c\u964d\u7ef4,\u5373\u5c06\u591a\u5e27\u4fe1\u606f\u878d\u5408\u4e3a\u5355\u5e27,\u518d\u8fdb\u884c\u540e\u7eed\u64cd\u4f5c.[26]<\/p>\n

BEV detection head:\u4fee\u6539Deformable DETR\u6784\u5efa\u4e86Two-stage BEV Decoder \u5982\u56fe(c)\uff0c\u91c7\u7528Content queries(\u751f\u6210sampling offsets\u548cattention weight\u7684query features\u7684\u53ef\u5b66\u4e60embedding)\u548c\u6df7\u5408\u53c2\u8003\u70b9\uff08\u4e00\u4e2a\u7ebf\u6027\u5c42\u4ece\u53ef\u5b66\u4e60positional embedding\u96c6\u5408\u9884\u6d4b\u5f97\u5230\u7684\u53c2\u8003\u70b9\u7ed3\u5408proposals\u6295\u5f71\u70b9\u751f\u6210\u7684\u53c2\u8003\u70b9\uff09\u4f5c\u4e3a\u8f93\u5165\u7684object query.<\/p>\n

Uniformer[27]<\/p>\n

\u8bba\u6587 \u4ee3\u7801 \u89e3\u8bfb[28]<\/p>\n

Uniformer\u6d41\u7a0b\u56fe<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a\u56fe\u50cf\uff08\u57fa\u4e8etransformer\uff09<\/p>\n

\u65f6\u5e8f\u878d\u5408\uff1a\u533a\u522b\u4e0eBEVFormer\u7684\u8fed\u4ee3\u878d\u5408\u524d\u4e00\u5e27\uff0cUniformer\u91c7\u7528\u5c06\u5386\u53f2\u5e27\u76f4\u63a5\u53d8\u6362\u5230\u5f53\u524d\u5e27\u65f6\u523b\uff0c\u5f62\u6210\u865a\u62df\u89c6\u89d2\uff0c\u5c06\u6240\u6709\u7684\u5386\u53f2\u56fe\u50cf\u4e00\u8d77\u63d0\u53d6BEV\u7279\u5f81\u3002(BEVFormer\u5728\u8fed\u4ee3\u878d\u5408\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u7531\u4e8e\u878d\u5408\u533a\u57df\u91cd\u53e0\u8fc7\u5c0f\u800c\u635f\u5931\u5386\u53f2\u5e27\u4fe1\u606f\u3002)<\/p>\n

\u91cd\u53e0\u533a\u57df\u53ca\u865a\u62df\u89c6\u89d2<\/p>\n

BEVFormer vs Uniformer\u65f6\u5e8f\u878d\u5408<\/p>\n

\u81ea\u52a8\u9a7e\u9a76\uff1aBEV\u5728\u7ebf\u5efa\u56fe<\/p>\n

\u6700\u8fd1\u4e00\u5e74\uff0c\u5305\u62ec\u534e\u4e3a\u3001\u5c0f\u9e4f\u3001\u6beb\u672b\u7b49\u5728\u5185\u7684\u8f66\u4f01\u8d8a\u6765\u8d8a\u591a\u7684\u63d0\u53ca\u91cd\u611f\u77e5\u8f7b\u5730\u56fe\u7684\u81ea\u52a8\u9a7e\u9a76\u843d\u5730\u65b9\u6848\u3002\u9020\u6210\u8fd9\u79cd\u73b0\u8c61\u7684\u539f\u56e0\u731c\u6d4b\u662f\uff1a\u56fd\u5185\u5934\u90e8\u56fe\u5546\u7684\u9ad8\u7cbe\u5730\u56fe\u8986\u76d6\u8303\u56f4\u548c\u66f4\u65b0\u9891\u7387\u96be\u4ee5\u9002\u5e94\u8f66\u4f01\u843d\u5730\u81ea\u52a8\u9a7e\u9a76\u7684\u8fdb\u5ea6\u3002\u6bd4\u5982\uff1a\u5728\u4e00\u4e9b\u4e8c\u7ebf\u3001\u7edd\u5927\u591a\u6570\u7684\u4e09\u3001\u56db\u3001\u4e94\u7ebf\u57ce\u5e02\u662f\u6ca1\u6709\u666e\u53ca\u9ad8\u7cbe\u5730\u56fe\u7684\uff1b\u5373\u4f7f\u5728\u4e00\u3001\u4e8c\u7ebf\u57ce\u5e02\uff0c\u8981\u5bf9\u9ad8\u7cbe\u5730\u56fe\u505a\u5230\u6708\u7ea7\u522b\u7684\u66f4\u65b0\uff0c\u5373\u4f7f\u4ed8\u51fa\u6781\u5927\u7684\u6210\u672c\u4ee3\u4ef7\u4e5f\u4e0d\u4e00\u5b9a\u80fd\u5b8c\u6210\uff0c\u6240\u4ee5\u8f66\u4f01\u843d\u5730\u81ea\u52a8\u9a7e\u9a76\u5c31\u5fc5\u987b\u9762\u4e34\u5730\u56fe\u7684\u73b0\u5b9e\u6027\u53d8\u5316\u3002\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u89e3\u51b3\u4e0a\u8ff0\u95ee\u9898\u7684\u65b9\u6848\u5c31\u662f\uff1a\u91cd\u611f\u77e5\uff0c\u5728\u8f66\u7aef\u5b9e\u65f6\u5728\u7ebf\u6784\u5efa\u5730\u56fe\u3002\u8f66\u9053\u7684\u5b9e\u65f6\u5efa\u56fe\u4e0a\u6e38\u4f9d\u8d56\u4e8eBEV\u611f\u77e5\u65b9\u6848\uff0c\u5728\u6b64\u57fa\u7840\u4e0a\u8fd8\u9700\u8981\u89e3\u51b3\u5730\u56fe\u8981\u7d20\u4e4b\u95f4\u7684\u62d3\u6251\u5173\u8054\uff0c\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u63d0\u51fa\u4e86polyline\u66ff\u4ee3raster\u7684\u5730\u56fe\u8868\u793a\u65b9\u6848\uff0c\u4f18\u7f3a\u70b9\u5982\u4e0b\uff1a<\/p>\n

\u5730\u56fe\u8868\u793a\u65b9\u6848\uff1araster->polyline[29]<\/p>\n

raster\u7684\u7f3a\u70b9\uff1a<\/p>\n

\u5728\u9884\u6d4b\u7684\u6805\u683c\u5316\u5730\u56fe\u4e2d\u5f88\u96be\u786e\u4fdd\u7a7a\u95f4\u4e00\u81f4\u6027\uff0c\u9644\u8fd1\u7684\u50cf\u7d20\u53ef\u80fd\u6709\u77db\u76fe\u7684\u8bed\u4e49\u7c7b\u522b\u6216\u51e0\u4f55\u5f62\u72b6\u3002<\/p>\n

\u4e8c\u7ef4\u6805\u683c\u5316\u5730\u56fe\u4e0e\u5927\u591a\u6570\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf\u4e0d\u517c\u5bb9\uff0c\u56e0\u4e3a\u8fd9\u4e9b\u7cfb\u7edf\u5728\u8f68\u8ff9\u9884\u6d4b\u548c\u89c4\u5212\u4e2d\u9700\u8981\u4f7f\u7528\u5b9e\u4f8b\u7ea7\u522b\u7684\u4e8c\u7ef4\/\u4e09\u7ef4\u77e2\u91cf\u5730\u56fe\u3002<\/p>\n

polyline\u7684\u4f18\u70b9\uff1a<\/p>\n

\u5229\u7528\u9876\u70b9\u6765\u7ea6\u675f\u51e0\u4f55\u5f62\u72b6\uff0c\u53ef\u4ee5\u8f7b\u677e\u7684\u8868\u793a\u9ad8\u7cbe\u5ea6\u5730\u56fe\u4e2d\u7684\u51e0\u4f55\u5143\u7d20\uff0c\u9876\u70b9\u7684\u987a\u5e8f\u53ef\u4ee5\u7f16\u7801\u5730\u56fe\u5143\u7d20\u65b9\u5411\u3002<\/p>\n

\u5df2\u88ab\u4e0b\u6e38\u7684\u81ea\u52a8\u9a7e\u9a76\u6a21\u5757\uff08\u8f68\u8ff9\u9884\u6d4b\uff09\u4f5c\u4e3a\u5730\u56fe\u7279\u5f81\u5e7f\u6cdb\u4f7f\u7528\u3002<\/p>\n

\u4e0b\u9762\u7b80\u5355\u4ecb\u7ecd\u51e0\u79cd\u57fa\u4e8eBEV\u7684\u8f66\u7aef\u5b9e\u65f6\u5efa\u56fe\u65b9\u6848\uff1a<\/p>\n

HDMapNet[30]<\/p>\n

HDMapNet\u67b6\u6784\u56fe<\/p>\n

\u8bba\u6587 \u4ee3\u7801<\/p>\n

\u5730\u56fe\u8868\u793a\u65b9\u6848\uff1araster<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a<\/p>\n

\u70b9\u4e91\uff1adynamic voxelization->PointPillar->PointNet->CNNs<\/p>\n

\u56fe\u50cf\uff1a\u63d0\u53d62d\u7279\u5f81->MLP\u5b66PV\u5230camera top-down\u6620\u5c04->\u7528\u5916\u53c2\u8f6c\u6362\u5230BEV(\u91cd\u53e0\u6805\u683c\u53d6\u7279\u5f81\u5747\u503c)<\/p>\n

\u8981\u7d20\u68c0\u6d4b\uff1a<\/p>\n

BEV\u7a7a\u95f4\u662f\u4e00\u4e2a\u5e26\u6709\u7279\u5f81\u7684\u6805\u683c\u56fe\uff0c\u7136\u540e\u5229\u7528\u5168\u5377\u79ef\u7f51\u7edc(FCN)\u53bb\u751f\u6210\u8bed\u4e49\u56fe\u3001\u5b9e\u4f8b\u56fe\u548c\u65b9\u5411\u56fe.<\/p>\n

\u62d3\u6251\u6784\u5efa\uff1a<\/p>\n

\u91c7\u7528\u4f20\u7edf\u7b97\u6cd5DBSCAN\u53bb\u805a\u7c7b\u5b9e\u4f8b\uff0c\u901a\u8fc7NMS\u53bb\u91cd\uff0c\u7136\u540e\u57fa\u4e8e\u65b9\u5411\u56fepixel\u7684\u65b9\u5411\u6765\u6784\u5efa\u77e2\u91cf\u3002<\/p>\n

VectorMapNet[31]<\/p>\n

<\/p>\n

VectorMapNet<\/p>\n

\u8d44\u6599\uff1a\u4ee3\u7801 \u8bba\u6587 \u89e3\u8bfb[32]<\/p>\n

\u5c40\u9650\uff1a\u5c3d\u7ba1\u6211\u4eec\u7684\u65b9\u6cd5\u53ef\u4ee5\u5728\u5355\u4e2a\u5e27\u4e2d\u751f\u6210\u8fde\u8d2f\u4e14\u590d\u6742\u7684\u51e0\u4f55\u56fe\u5f62\uff0c\u4f46\u4e0d\u80fd\u4fdd\u8bc1\u65f6\u5e8f\u4e00\u81f4\u7684\u9884\u6d4b\u3002<\/p>\n

\u5730\u56fe\u8868\u793a\u65b9\u6848\uff1apolyline<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a<\/p>\n

\u70b9\u4e91\uff1adynamic voxelization->PointPillar<\/p>\n

\u56fe\u50cf\uff1a\u7528ResNet\u63d0\u53d6PV\u7279\u5f81\uff0c\u901a\u8fc7IPM+\u5730\u9762\u9ad8\u5ea6\u63d2\u503c(-1m,0m,1m,2m) concate\u7684\u65b9\u5f0f\u5b9e\u73b0PV2BEV\u8f6c\u6362\u3002<\/p>\n

\u878d\u5408\uff1a\u70b9\u4e91\u3001\u56fe\u50cf\u7279\u5f81concate\u540e+2\u5c42\u7684\u5377\u79ef\u7f51\u7edc\u751f\u6210BEV\u7279\u5f81<\/p>\n

\u8981\u7d20\u68c0\u6d4b\uff1a<\/p>\n

\u91c7\u7528transformer decoder\u548c\u4e00\u4e2a\u9884\u6d4b\u5934\uff0c\u901a\u8fc7\u4e8c\u5206\u56fe\u5339\u914d\u635f\u5931\u8fdb\u884c\u8bad\u7ec3\uff0c\u907f\u514d\u7c7b\u4f3cNMS\u7684\u540e\u5904\u7406\u6b65\u9aa4\u3002<\/p>\n

transformer decoder\uff1a\u6bcf\u4e2a\u5143\u7d20query\u7531k\u4e2a\u5173\u952e\u70b9\u7ec4\u6210\uff0c\u5c06\u5143\u7d20query\u62c9\u4f38\u6210\u4e00\u4e2a\u5e8f\u5217\u8f93\u5165\u7ed9transformer decoder\uff0cdecoder\u7684Cross-attention\u91c7\u7528bev feature\u4f5c\u4e3akey\u548cvalue\uff0c\u4f7f\u7528deformable attention\uff0c\u6bcf\u4e2aquery\u5728 BEV \u7279\u5f81\u4e2d\u90fd\u6709\u4e00\u4e2a 2D \u4f4d\u7f6e\uff0c\u4ee5\u5728\u9884\u6d4b\u7684\u5173\u952e\u70b9\u548c BEV \u7a7a\u95f4\u4e4b\u95f4\u5efa\u7acb\u4e00\u4e00\u5bf9\u5e94\u5173\u7cfb\u5e76\u52a0\u901f\u8bad\u7ec3\u6536\u655b\u3002<\/p>\n

\u9884\u6d4b\u5934\uff1a\u4e00\u4e2a\u56de\u5f52\u5206\u652f\u548c\u4e00\u4e2a\u5206\u7c7b\u5206\u652f\u3002\u56de\u5f52\u5206\u652f\uff1a\u5bf9\u4e8e\u6bcf\u4e2a\u5730\u56fe\u5143\u7d20\uff0c\u56de\u5f52\u5206\u652f\u901a\u8fc7\u5171\u4eab\u7684MLP\u4ece\u5173\u952e\u70b9embedding\u9884\u6d4b\u6bcf\u4e2a\u5173\u952e\u70b9\u7684\u5750\u6807\u3002\u5206\u7c7b\u5206\u652f\uff1aconcat\u5355\u4e2a\u5730\u56fe\u5143\u7d20\u7684\u5173\u952e\u70b9embeddings\uff0c\u5e76\u901a\u8fc7\u7ebf\u6027\u6295\u5f71\u5c42\u9884\u6d4b\u7c7b\u522b\u6807\u7b7e\u3002\u6bcf\u4e2a\u5173\u952e\u70b9\u6709\u4e24\u4e2alearnable embeddings\uff1a\u4f4d\u7f6eembedding + \u5f52\u5c5eembedding\u3002<\/p>\n

\u62d3\u6251\u6784\u5efa<\/p>\n

\u5c06\u6bcf\u4e2a\u5730\u56fe\u5143\u7d20\u5173\u952e\u70b9\u96c6\u7684x\u3001y\u5750\u6807\u3001\u7c7b\u522b\u8fdb\u884c\u5e8f\u5217\u5316\uff0c\u4f5c\u4e3aTransformer\u89e3\u7801\u5668\u7684query\u8f93\u5165\uff0c\u7136\u540e\u5c06\u4e00\u7cfb\u5217\u9876\u70b9token\u8fed\u4ee3\u5730\u8f93\u5165\u5230Transformer\u89e3\u7801\u5668\u4e2d\uff0c\u5e76\u548cBEV\u7279\u5f81\u505across-attention\uff0c\u901a\u8fc7\u7ebf\u6027\u6295\u5f71\u5c42\u8fed\u4ee3\u5730\u5c06\u8fd9\u4e9bquery\u89e3\u7801\u4e3a\u9876\u70b9\u5750\u6807\u503c\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u4f1a\u9884\u6d4b\u4e0b\u4e00\u4e2a\u9876\u70b9\u5750\u6807\u7684\u5206\u5e03\u53c2\u6570\uff0c\u5e76\u9884\u6d4bEnd of Sequence token (EOS) \u6765\u7ec8\u6b62\u5e8f\u5217\u751f\u6210\u3002<\/p>\n

\u91c7\u7528\u81ea\u56de\u5f52\u7f51\u7edc\u5c06\u8981\u7d20\u7684Polyline\u5efa\u6a21\u6210\u6761\u4ef6\u8054\u5408\u6982\u7387\u5206\u5e03\uff0c\u5e76\u8f6c\u6362\u6210\u4e00\u7cfb\u5217\u7684\u9876\u70b9\u5750\u6807\u7684\u6761\u4ef6\u5206\u5e03\u4e58\u79ef\uff1a<\/p>\n

Polyline Generator\u5efa\u6a21SuperFusion[33]<\/p>\n

SuperFusion\u67b6\u6784\u56fe<\/p>\n

\u8d44\u6599\uff1a\u4ee3\u7801 \u8bba\u6587 \u89e3\u8bfb[34]<\/p>\n

\u5730\u56fe\u8868\u793a\u65b9\u6848\uff1araster<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a<\/p>\n

\u56fe\u50cf\uff1a\u5c06\u70b9\u4e91\u6295\u5f71\u5230\u56fe\u50cf\u5e73\u9762\u4e3a\u89c6\u89c9RGB\u8d4b\u503c\u6df1\u5ea6\uff0c\u5206\u522b\u63d0\u53d6\u56fe\u50cf\u7279\u5f81\u548c\u4f30\u8ba1\u6df1\u5ea6\u5206\u5e03\uff0c\u5c06\u6df1\u5ea6\u56fe\u5206\u5e03\u548c\u56fe\u50cf\u7279\u5f81\u5916\u79ef\u5f97\u5230\u89c6\u690e\u4f53\u7279\u5f81\uff0c\u518d\u901a\u8fc7voxel pooling\u65b9\u5f0f\u751f\u6210BEV\u7279\u5f81\u3002(lidar\u6df1\u5ea6\u56fe\u63d2\u503c\u76d1\u7763\u6df1\u5ea6\u5206\u5e03\u4f30\u8ba1)<\/p>\n

\u70b9\u4e91\uff1adynamic voxelization->PointPillar\uff0c\u540cVectorMapNet.<\/p>\n

\u7279\u5f81\u878d\u5408[35]\uff1a<\/p>\n

Data-level\uff1a\u70b9\u4e91sparse\u6df1\u5ea6\u56fe\u548c\u56fe\u50cfconcatenate\uff0c\u5e76\u7528dense\u6df1\u5ea6\u56fe\u8fdb\u884c\u76d1\u7763\u8bad\u7ec3<\/p>\n

Feature-level\uff1a\u5728\u957f\u8ddd\u79bb\u4e0a\uff0c\u5c06FV\u56fe\u50cf\u7279\u5f81\u4f5c\u4e3aK\u548cV\uff0c\u5c06\u70b9\u4e91\u7684BEV\u7279\u5f81\u4f5c\u4e3aQ\u53bb\u548c\u56fe\u50cf\u7279\u5f81\u505across-attention\u5f97\u5230\u65b0\u7684BEV\u7279\u5f81\uff0c\u5e76\u7ecf\u8fc7\u4e00\u7cfb\u5217\u5377\u79ef\u64cd\u4f5c\u5f97\u5230\u6700\u7ec8\u7684\u70b9\u4e91BEV<\/p>\n

BEV-level\uff1a\u5c06\u56fe\u50cf\u548c\u70b9\u4e91BEV\u7279\u5f81\u5148concat\u540e\u5b66\u4e60flow field\uff0c\u7136\u540e\u5c06\u8be5flow field\u7528\u4e8e\u56fe\u50cfBEV\u7279\u5f81\u7684warp\uff08\u6839\u636e\u6bcf\u4e2a\u70b9\u7684flow field\u8fdb\u884c\u53cc\u7ebf\u6027\u63d2\u503c\uff09\uff0c\u4ece\u800c\u751f\u6210\u5bf9\u9f50\u540e\u7684\u56fe\u50cfBEV\u7279\u5f81<\/p>\n

\u8981\u7d20\u68c0\u6d4b:\uff08\u540cHDMapNet\uff09<\/p>\n

BEV\u7a7a\u95f4\u662f\u4e00\u4e2a\u5e26\u6709\u7279\u5f81\u7684\u6805\u683c\u56fe\uff0c\u7136\u540e\u5229\u7528\u5168\u5377\u79ef\u7f51\u7edc(FCN)\u53bb\u751f\u6210\u8bed\u4e49\u56fe\u3001\u5b9e\u4f8b\u56fe\u548c\u65b9\u5411\u56fe.<\/p>\n

\u62d3\u6251\u6784\u5efa\uff1a\uff08\u540cHDMapNet\uff09<\/p>\n

\u91c7\u7528\u4f20\u7edf\u7b97\u6cd5DBSCAN\u53bb\u805a\u7c7b\u5b9e\u4f8b\uff0c\u901a\u8fc7NMS\u53bb\u91cd\uff0c\u7136\u540e\u57fa\u4e8e\u65b9\u5411\u56fepixel\u7684\u65b9\u5411\u6765\u6784\u5efa\u77e2\u91cf\u3002<\/p>\n

MapTR[36]<\/p>\n

MapTR\u67b6\u6784\u56fe<\/p>\n

\u8d44\u6599\uff1a\u4ee3\u7801 \u8bba\u6587 \u8bd1\u6587 \u4f5c\u8005\u5f00\u8bb2[37]<\/p>\n

\u5c40\u9650\uff1a\u76ee\u524d\u53ea\u5904\u7406\u5355\u5e27\u6570\u636e\uff0c\u540cVectorMapNet\u4e0d\u80fd\u4fdd\u8bc1\u65f6\u5e8f\u7684\u591a\u5e27\u89c6\u9891\u56fe\u50cf\uff0c\u80fd\u591f\u6709\u4e00\u81f4\u7684\u9884\u6d4b\u3002<\/p>\n

\u5730\u56fe\u8868\u793a\u65b9\u6848\uff1apolyline<\/p>\n

\u7279\u5f81\u63d0\u53d6\uff1a<\/p>\n

\u56fe\u50cfBEV\u7279\u5f81\u63d0\u53d6<\/p>\n

\u56fe\u50cf\uff1a\u91c7\u7528GKT\u5b9e\u73b0PV2BEV\u8f6c\u6362\u3002\u5bf9\u4e8eBEV Query\u53d6\u5730\u9762\u9ad8\u5ea6\u7ec4\u6210\u70b9(x,y,z)\uff0c\u901a\u8fc7\u76f8\u673a\u5185\u5916\u53c2\u83b7\u53d6\u5bf9\u5e94\u5404\u4e2a\u89c6\u89d2\u56fe\u50cf\u5750\u6807(u,v)\uff0c\u5e76\u4e14\u63d0\u53d6\u9644\u8fd1\u7684Kh\u00d7Kw\u6838\u533a\u57df\u7684\u7279\u5f81\uff0c\u7136\u540e\u505aCross-Attention\u5f97\u5230BEV\u7279\u5f81\u3002<\/p>\n

\u8981\u7d20\u68c0\u6d4b&\u62d3\u6251\u6784\u5efa\uff1a<\/p>\n

\u91c7\u7528point query + instance query\u878d\u5408\u540e\u4e0eBEV\u7279\u5f81cross-attention\uff0c\u5e76\u4e14 query\u4e4b\u95f4\u8fdb\u884c\u5b9e\u4f8b\u5185\u548c\u5b9e\u4f8b\u95f4\u7684\u4ea4\u4e92\uff0c\u5728\u8ba1\u7b97loss\u7684\u8fc7\u7a0b\u4e2d\u91c7\u7528point match\u548cinstance match\u6765\u83b7\u53d6\u771f\u503c\u4e0e\u9884\u6d4b\u7684\u5173\u8054\u5173\u7cfb\u3002\u5206\u5c42\u7ea7query\u8fed\u4ee3\u5730\u66f4\u65b0\uff0c\u6700\u7ec8\u8f93\u51fa\u5730\u56fe\u8981\u7d20\u7684\u8bed\u4e49\u7c7b\u522b\u548c\u8282\u70b9\u4f4d\u7f6e\u3002<\/p>\n

MapTR\u5f15\u5165\u5206\u5c42\u7ea7\u5339\u914d\u673a\u5236\u8fdb\u884c\u6807\u7b7e\u5206\u914d\u3002\u5b9e\u4f8b\u7ea7\u5339\u914d\uff1a\u5728\u9884\u6d4b\u7684\u5730\u56fe\u8981\u7d20\u548c\u771f\u5b9e\u7684\u5730\u56fe\u8981\u7d20\u4e4b\u95f4\u627e\u5230\u6700\u4f18\u7684\u4e00\u4e00\u5bf9\u5e94\u5173\u7cfb \u03c0 \uff0c\u5339\u914d\u7684\u4f9d\u636e\u4e3a\u5730\u56fe\u8981\u7d20\u4e4b\u95f4\u7684\u7c7b\u522b\u76f8\u5173\u6027\u548c\u4f4d\u7f6e\u76f8\u5173\u6027\u3002\u8282\u70b9\u7ea7\u5339\u914d\uff1a\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u5730\u56fe\u8981\u7d20\uff0c\u4ece\u7b49\u6548\u6392\u5217\u96c6\u5408\u4e2d\u627e\u5230\u6700\u4f18\u7684\u6392\u5217\u03b3\uff0c\u4f7f\u5f97\u9884\u6d4b\u7684\u8282\u70b9\u548c\u771f\u5b9e\u7684\u8282\u70b9\u4e00\u4e00\u5bf9\u5e94\u4e14\u70b9\u5bf9\u4e4b\u95f4\u7684\u8ddd\u79bb\u4e4b\u548c\u6700\u5c0f\u3002<\/p>\n

\u635f\u5931\u51fd\u6570\uff1a\u6839\u636e\u5206\u5c42\u7ea7\u5339\u914d\u7684\u7ed3\u679c\uff0c\u635f\u5931\u51fd\u6570\u7531classification loss\uff0cpoint2point loss\u548cedge direction loss\u7ec4\u6210\uff1aClassification loss\u6839\u636e\u5b9e\u4f8b\u7ea7\u5339\u914d\u7684\u7ed3\u679c\u5bf9\u5730\u56fe\u8981\u7d20\u7684\u7c7b\u522b\u9884\u6d4b\u8fdb\u884c\u7ea6\u675f\uff0cPoint2point loss\u6839\u636e\u8282\u70b9\u7ea7\u5339\u914d\u7684\u7ed3\u679c\uff0c\u5bf9\u5339\u914d\u7684\u70b9\u5bf9\u4e4b\u95f4\u7684\u4f4d\u7f6e\u5173\u7cfb\u8fdb\u884c\u7ea6\u675f\uff0cEdge direction loss\u5bf9\u9884\u6d4b\u7684edge\u548c\u771f\u5b9e\u7684edge\u7684\u65b9\u5411\u4e00\u81f4\u6027\u8fdb\u884c\u7ea6\u675f\u3002<\/p>\n

\u5c0f\u7ed3\uff1a<\/p>\n

FSD\u8f66\u9053\u7ebf\u62d3\u6251\u5173\u7cfb\u611f\u77e5<\/p>\n

1.\u5728BEV\u611f\u77e5\u4e2d\uff0c\u901a\u8fc7\u65f6\u5e8f\u878d\u5408\u53ef\u4ee5\u5f25\u8865\u5355\u5e27\u611f\u77e5\u7684\u5c40\u9650\u6027,\u589e\u52a0\u611f\u53d7\u91ce,\u6539\u5584\u76ee\u6807\u68c0\u6d4b\u5e27\u95f4\u8df3\u53d8\u548c\u76ee\u6807\u906e\u6321\u95ee\u9898,\u66f4\u52a0\u51c6\u786e\u5730\u5224\u65ad\u76ee\u6807\u8fd0\u52a8\u901f\u5ea6,\u662f\u63d0\u9ad8\u611f\u77e5\u7b97\u6cd5\u51c6\u786e\u6027\u548c\u8fde\u7eed\u6027\u7684\u5173\u952e\u3002\u4f46\u662f\u4e0a\u8ff0\u7684\u8f66\u7aef\u5efa\u56fe\u65b9\u6848\u591a\u662f\u5355\u5e27\u5efa\u56fe\uff0c\u4e0d\u80fd\u4fdd\u8bc1\u65f6\u5e8f\u7684\u591a\u5e27\u89c6\u9891\u56fe\u50cf\u80fd\u591f\u6709\u4e00\u81f4\u7684\u9884\u6d4b\u3002\u4e0a\u8ff0\u7684\u8bba\u6587\u4e2d\u4e5f\u90fd\u63d0\u5230\u4e86\u540e\u7eed\u7684\u7814\u7a76\u65b9\u5411\u5c31\u662f\u65f6\u5e8f\u878d\u5408\u5efa\u56fe\uff0c\u6240\u4ee5\u8fd9\u79cd\u60c5\u51b5\u53ef\u80fd\u4f1a\u57282023\u5e74\u5f97\u5230\u89e3\u51b3\u3002<\/p>\n

2.\u53ef\u4ee5\u770b\u5230\u4e0a\u8ff0\u5efa\u56fe\u65b9\u6848\u7684\u5c55\u793a\u89c6\u9891\u6ca1\u6709\u8fc7\u591a\u5c55\u793a\u5728\u9053\u8def\u5206\u79bb\u5408\u5e76\u3001\u531d\u9053\u7b49\u62d3\u6251\u590d\u6742\u573a\u666f\u7684\u5efa\u56fe\u6548\u679c\uff0c\u8fd9\u70b9\u548ctesla\u5c55\u793a\u7684\u5728\u7ebf\u5efa\u56fe\u8fd8\u662f\u6709\u5dee\u8ddd\u3002\u4ecetesla\u7684\u8f93\u5165\u6570\u636e\u662fvideo module\u7684\u65f6\u5e8f\u878d\u5408\u7279\u5f81\u548c\u5bfc\u822a\u5730\u56fe\u6765\u770b\uff0c\u540e\u7eed\u5bfc\u822a\u5730\u56fe\u4e5f\u53ef\u80fd\u52a0\u5165\u5230\u878d\u5408\u4e2d\u3002<\/p>\n

3.\u7ec8\u5c40\u731c\u60f3\uff1a\u8f66\u7aef\u5b9e\u65f6\u521b\u5efa\u8f66\u9053\u62d3\u6251\u5730\u56fe\uff0c\u5728\u6ca1\u6709\u5730\u56fe\u7684\u533a\u57df\u63d0\u4f9b\u89c4\u5212\u51b3\u7b56\u4f7f\u7528\uff0c\u540c\u65f6\u5c06\u62d3\u6251\u4fe1\u606f\u56de\u4f20\uff1b\u5728\u6709\u5730\u56fe\u7684\u533a\u57df\uff0c\u68c0\u6d4b\u5230\u66f4\u65b0\u53d8\u5316\u5b9e\u65f6\u878d\u5408\uff0c\u5e76\u56de\u4f20\u4e91\u7aef\u6821\u9a8c\u3002\u8fd9\u6837\u5c31\u540c\u65f6\u89e3\u51b3\u4e86\u5730\u56fe\u7f3a\u5931\u548c\u9c9c\u5ea6\u4e24\u4e2a\u5173\u952e\u95ee\u9898\u3002<\/p>\n

\u6ce8\uff1a<\/p>\n

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