Comprehensive perception of the environment is crucial for the safe operation
of autonomous vehicles. However, the perception capabilities of autonomous
vehicles are limited due to occlusions, limited sensor ranges, or environmental
influences. Collective Perception (CP) aims to mitigate these problems by
enabling the exchange of information between vehicles. A major challenge in CP
is the fusion of the exchanged information. Due to the enormous bandwidth
requirement of early fusion approaches and the interchangeability issues of
intermediate fusion approaches, only the late fusion of shared detections is
practical. Current late fusion approaches neglect valuable information for
local detection, this is why we propose a novel fusion method to fuse the
detections of cooperative vehicles within the local LiDAR-based detection
pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends
the PV-RCNN++ framework to fuse collective detections. Code is available at
this https URL

自动驾驶汽车的安全操作需要对环境进行全面感知，而其感知能力受到遮挡、传感器范围限制或环境影响的限制。集体感知旨在通过车辆间的信息交换来减轻这些问题，其中融合交换信息是一个主要挑战。由于早期融合方法对带宽需求巨大以及中间融合方法的可替代性问题，只有共享检测的后期融合是可行的。然而，现有的后期融合方法忽视了本地检测的有价值信息，因此我们提出了一种新颖的融合方法来融合协作车辆在本地基于激光雷达的检测中的检测结果，即集体 PV-RCNN（CPV-RCNN），该方法扩展了 PV-RCNN++ 框架来融合集体检测。此外，代码可在此 https URL 中找到。

集体 PV-RCNN：利用集体检测的新型融合技术增强基于局部 LiDAR 的感知

Collective PV-RCNN: A Novel Fusion Technique using Collective Detections  for Enhanced Local LiDAR-Based Perception

Current efficient LiDAR-based detection frameworks are lacking in exploiting
object relations, which naturally present in both spatial and temporal manners.
To this end, we introduce a simple, efficient, and effective two-stage
detector, termed as Ret3D. At the core of Ret3D is the utilization of novel
intra-frame and inter-frame relation modules to capture the spatial and
temporal relations accordingly. More Specifically, intra-frame relation module
(IntraRM) encapsulates the intra-frame objects into a sparse graph and thus
allows us to refine the object features through efficient message passing. On
the other hand, inter-frame relation module (InterRM) densely connects each
object in its corresponding tracked sequences dynamically, and leverages such
temporal information to further enhance its representations efficiently through
a lightweight transformer network. We instantiate our novel designs of IntraRM
and InterRM with general center-based or anchor-based detectors and evaluate
them on Waymo Open Dataset (WOD). With negligible extra overhead, Ret3D
achieves the state-of-the-art performance, being 5.5% and 3.2% higher than the
recent competitor in terms of the LEVEL 1 and LEVEL 2 mAPH metrics on vehicle
detection, respectively.

Ret3D 是一个简单和有效的两阶段检测器，核心是利用新型的内帧和间帧关系模块来捕捉空间和时间关系。通过在通用基于中心或基于锚点的检测器上实例化我们的新设计，我们可以在 Waymo 开放数据集上获得最先进的性能。

Ret3D: 重新思考物体关系实现驾驶场景中高效的 3D 物体检测

Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes

Stereo-based 3D detection aims at detecting 3D object bounding boxes from
stereo images using intermediate depth maps or implicit 3D geometry
representations, which provides a low-cost solution for 3D perception. However,
its performance is still inferior compared with LiDAR-based detection
algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based
models can encode accurate object boundaries and surface normal directions from
LiDAR point clouds. However, the detection results of stereo-based detectors
are easily affected by the erroneous depth features due to the limitation of
stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry
Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of
high-level geometry-aware representations of LiDAR-based detection models. In
addition, we found existing voxel-based stereo detectors failed to learn
semantic features effectively from indirect 3D supervisions. We attach an
auxiliary 2D detection head to provide direct 2D semantic supervisions.
Experiment results show that the above two strategies improved the geometric
and semantic representation capabilities. Compared with the state-of-the-art
stereo detector, our method has improved the 3D detection performance of cars,
pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official
KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is
further narrowed.

提出了基于 LiDAR 几何感知和 2D 语义监督的低成本、高性能的 LIGA-Stereo 模型，通过学习 LiDAR 模型的高级几何感知表示来改进立体声检测器的 3D 感知，并直接提供 2D 语义监督以提高语义表示能力，实验结果表明在车辆、行人、骑车者的 3D 检测 mAP 方面得到了显著的提高。