论文标题
cagroup3d：用于点云上3D对象检测的班级感知分组
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
论文作者
论文摘要
我们提出了一个新颖的两阶段完全稀疏的卷积3D对象检测框架，名为Cagroup3d。我们提出的方法首先通过使用相同的语义预测来利用对象表面体素的班级感知的本地组策略来生成一些高质量的3D建议，这些策略考虑了语义一致性和在先前的自下而上方法中放弃的语义一致性和不同的位置。然后，为了恢复由于不正确的体素分割而导致的错素的特征，我们构建了一个完全稀疏的卷积ROI池池模块，以直接从骨架中直接汇总细粒的空间信息，以进一步提出建议。它是内存和计算有效的，可以更好地编码每个3D建议的几何特征。我们的模型在扫描仪V2和 +\ textit {2.6} \％的sun rgb-d上以[email protected]的sun rgb-d上的 +\ textit {3.6 \％}获得了最新的3D检测性能。代码将在https://github.com/haiyang-w/cagroup3d上找到。
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in previous bottom-up approaches. Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module to directly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific features of each 3D proposal. Our model achieves state-of-the-art 3D detection performance with remarkable gains of +\textit{3.6\%} on ScanNet V2 and +\textit{2.6}\% on SUN RGB-D in term of [email protected]. Code will be available at https://github.com/Haiyang-W/CAGroup3D.
