Current 3D object detection methods for indoor scenes mainly follow the
voting-and-grouping strategy to generate proposals. However, most methods
utilize instance-agnostic groupings, such as ball query, leading to
inconsistent semantic information and inaccurate regression of the proposals.
To this end, we propose a novel superpoint grouping network for indoor
anchor-free one-stage 3D object detection. Specifically, we first adopt an
unsupervised manner to partition raw point clouds into superpoints, areas with
semantic consistency and spatial similarity. Then, we design a geometry-aware
voting module that adapts to the centerness in anchor-free detection by
constraining the spatial relationship between superpoints and object centers.
Next, we present a superpoint-based grouping module to explore the consistent
representation within proposals. This module includes a superpoint attention
layer to learn feature interaction between neighboring superpoints, and a
superpoint-voxel fusion layer to propagate the superpoint-level information to
the voxel level. Finally, we employ effective multiple matching to capitalize
on the dynamic receptive fields of proposals based on superpoints during the
training. Experimental results demonstrate our method achieves state-of-the-art
performance on ScanNet V2, SUN RGB-D, and S3DIS datasets in the indoor
one-stage 3D object detection. Source code is available at
this https URL

提出了一种用于室内场景的新型超点分组网络，通过对原始点云进行超点划分，利用几何感知的投票模块调整超点和物体中心之间的空间关系，采用超点注意力层和超点 - 体素融合层来探索提案内的一致性表示，利用超点来基于动态感受野进行有效的多次匹配，实现室内一阶段 3D 物体检测的最新性能。