Regarding intelligent transportation systems, low-bitrate transmission via lossy point cloud compression is vital for facilitating real-time collaborative perception among connected agents, such as vehicles and infrastructures, under restricted bandwidth. In existing compression transmission systems, the sender lossily compresses point coordinates and reflectance to generate a transmission code stream, which faces transmission burdens from reflectance encoding and limited detection robustness due to information loss. To address these issues, this paper proposes a 3D object detection framework with reflectance prediction-based knowledge distillation (RPKD). We compress point coordinates while discarding reflectance during low-bitrate transmission, and feed the decoded non-reflectance compressed point clouds into a student detector. The discarded reflectance is then reconstructed by a geometry-based reflectance prediction (RP) module within the student detector for precise detection. A teacher detector with the same structure as the student detector is designed for performing reflectance knowledge distillation (RKD) and detection knowledge distillation (DKD) from raw to compressed point clouds. Our cross-source distillation training strategy (CDTS) equips the student detector with robustness to low-quality compressed data while preserving the accuracy benefits of raw data through transferred distillation knowledge. Experimental results on the KITTI and DAIR-V2X-V datasets demonstrate that our method can boost detection accuracy for compressed point clouds across multiple code rates. We will release the code publicly at https://github.com/HaoJing-SX/RPKD.

翻译：在智能交通系统中，通过有损点云压缩进行低比特率传输对于在受限带宽下促进车辆与基础设施等连接智能体间的实时协同感知至关重要。现有压缩传输系统中，发送方对点坐标与反射率进行有损压缩以生成传输码流，但面临反射率编码带来的传输负担以及信息损失导致的检测鲁棒性受限问题。为解决上述问题，本文提出一种基于反射率预测知识蒸馏（RPKD）的三维目标检测框架。我们在低比特率传输中压缩点坐标并舍弃反射率，将解码后的无反射率压缩点云输入学生检测器。随后，通过学生检测器内基于几何的反射率预测（RP）模块重建被舍弃的反射率，以实现精确检测。我们设计了与学生检测器结构相同的教师检测器，用于执行从原始点云到压缩点云的反射率知识蒸馏（RKD）与检测知识蒸馏（DKD）。所提出的跨源蒸馏训练策略（CDTS）使学生检测器具备对低质量压缩数据的鲁棒性，同时通过迁移的蒸馏知识保留原始数据的精度优势。在KITTI与DAIR-V2X-V数据集上的实验结果表明，本方法能在多种码率下提升压缩点云的检测精度。代码将公开于https://github.com/HaoJing-SX/RPKD。