  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_ADDRESS>Code will be available at https://github.com/Haiyang-W/CAGroup3D.
