Two-stage detectors have gained much popularity in 3D object detection. Most
two-stage 3D detectors utilize grid points, voxel grids, or sampled keypoints
for RoI feature extraction in the second stage. Such methods, however, are
inefficient in handling unevenly distributed and sparse outdoor points. This
paper solves this problem in three aspects. 1) Dynamic Point Aggregation. We
propose the patch search to quickly search points in a local region for each 3D
proposal. The dynamic farthest voxel sampling is then applied to evenly sample
the points. Especially, the voxel size varies along the distance to accommodate
the uneven distribution of points. 2) RoI-graph Pooling. We build local graphs
on the sampled points to better model contextual information and mine point
relations through iterative message passing. 3) Visual Features Augmentation.
We introduce a simple yet effective fusion strategy to compensate for sparse
LiDAR points with limited semantic cues. Based on these modules, we construct
our Graph R-CNN as the second stage, which can be applied to existing one-stage
detectors to consistently improve the detection performance. Extensive
experiments show that Graph R-CNN outperforms the state-of-the-art 3D detection
models by a large margin on both the KITTI and Waymo Open Dataset. And we rank
first place on the KITTI BEV car detection leaderboard. Code will be available
at https://github.com/Nightmare-n/GraphRCNN.

本文提出了一种名为 Graph R-CNN 的二阶段三维目标检测器，它使用动态点聚合、RoI-graph 池化和视觉特征增强等方法，解决了现有二阶段三维目标检测器处理不均匀分布和稀疏室外点时的效率问题，并在 KITTI 和 Waymo Open Dataset 上大幅优于现有模型。

基于语义装饰局部图的精确三维目标检测的 Graph R-CNN

Graph R-CNN: Towards Accurate 3D Object Detection with  Semantic-Decorated Local Graph

We propose a novel scene graph generation model called Graph R-CNN, that is
both effective and efficient at detecting objects and their relations in
images. Our model contains a Relation Proposal Network (RePN) that efficiently
deals with the quadratic number of potential relations between objects in an
image. We also propose an attentional Graph Convolutional Network (aGCN) that
effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and
realistic than existing metrics. We report state-of-the-art performance on
scene graph generation as evaluated using both existing and our proposed
metrics.

提出了一种新颖的场景图生成模型 ——Graph R-CNN，其在检测图像中的对象和它们之间的关系方面既有效又高效。该模型包含一种关系提议网络（RePN），能够有效地处理图像中潜在关系的二次数量。同时，我们提出了一种注意力图卷积网络（aGCN），有效地捕捉对象和关系之间的上下文信息。最后，我们引入了一种比现有指标更全面、更实际的评估方法。我们使用现有指标和我们提出的评估方法评估场景图生成，获得了最先进的性能。