Accurate and effective 3D object detection is critical for ensuring the
driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D
object detectors have exhibited promising performance. However, these methods
refine proposals individually, ignoring the rich contextual information in the
object relationships between the neighbor proposals. In this study, we
introduce an object relation module, consisting of a graph generator and a
graph neural network (GNN), to learn the spatial information from certain
patterns to improve 3D object detection. Specifically, we create an
inter-object relationship graph based on proposals in a frame via the graph
generator to connect each proposal with its neighbor proposals. Afterward, the
GNN module extracts edge features from the generated graph and iteratively
refines proposal features with the captured edge features. Ultimately, we
leverage the refined features as input to the detection head to obtain
detection results. Our approach improves upon the baseline PV-RCNN on the KITTI
validation set for the car class across easy, moderate, and hard difficulty
levels by 0.82%, 0.74%, and 0.58%, respectively. Additionally, our method
outperforms the baseline by more than 1% under the moderate and hard levels BEV
AP on the test server.

通过引入对象关系模块和图神经网络（GNN），从周围建议中提取空间信息并改进 3D 对象检测，该方法在 KITTI 数据集上相较基准方法 PV-RCNN 在简单、中等和困难难度级别的汽车类上分别提升了 0.82%、0.74% 和 0.58%，在中等和困难难度级别的 BEV AP 上优于基准方法超过 1%。

GraphRelate3D：上下文相关的基于关系图的三维物体检测

GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object  Relationship Graphs

Although it is well believed for years that modeling relations between
objects would help object recognition, there has not been evidence that the
idea is working in the deep learning era. All state-of-the-art object detection
systems still rely on recognizing object instances individually, without
exploiting their relations during learning.
This work proposes an object relation module. It processes a set of objects
simultaneously through interaction between their appearance feature and
geometry, thus allowing modeling of their relations. It is lightweight and
in-place. It does not require additional supervision and is easy to embed in
existing networks. It is shown effective on improving object recognition and
duplicate removal steps in the modern object detection pipeline. It verifies
the efficacy of modeling object relations in CNN based detection. It gives rise
to the first fully end-to-end object detector.

本研究提出一种基于对象关系模块的深度学习模型，可以同时处理对象之间的外观特征和几何关系，从而提高了对象识别和重复去除步骤，在 CNN-based detection 中显著地有效，且无需额外 supervision，是第一个完全端到端的物体检测器。