Efficient 3D LiDAR point cloud compression (LPCC) and streaming are critical for edge server-assisted robotic systems, enabling real-time communication with compact data representations. A widely adopted approach represents LiDAR point clouds as range images, enabling the direct use of mature image and video compression codecs. However, because these codecs are designed with human visual perception in mind, they often compromise geometric details, which downgrades the performance of downstream robotic tasks such as mapping and object detection. Furthermore, rate-distortion optimization (RDO)-based rate control remains largely underexplored for range image compression (RIC) under dynamic bandwidth conditions. To address these limitations, we propose D-Compress, a new detail-preserving and fast RIC framework tailored for real-time streaming. D-Compress integrates both intra- and inter-frame prediction with an adaptive discrete wavelet transform approach for precise residual compression. Additionally, we introduce a new RDO-based rate control algorithm for RIC through new rate-distortion modeling. Extensive evaluations on various datasets demonstrate the superiority of D-Compress, which outperforms state-of-the-art (SOTA) compression methods in both geometric accuracy and downstream task performance, particularly at compression ratios exceeding 100x, while maintaining real-time execution on resource-constrained hardware. Moreover, evaluations under dynamic bandwidth conditions validate the robustness of its rate control mechanism.
Efficient and Privacy-Protecting Background Removal for 2D Video Streaming using iPhone 15 Pro Max LiDAR
Department of Computer Science, Iowa State University, Ames, IA, USA（计算机科学系，爱荷华州立大学）；Department of Computer Science and School of Education, Iowa State University, Ames, IA, USA（计算机科学系与教育学院，爱荷华州立大学）
本研究利用iPhone 15 Pro Max的LiDAR技术，结合GPU图像处理与Metal着色器实现60帧/秒的高效背景去除，该方法不受光照影响，仅受深度数据带宽和LiDAR材质反射局限，未来分辨率提升后有望成为主流背景去除方案。
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding
Waymo LLC（Waymo有限责任公司）
激光雷达是广泛用于自动驾驶和增强现实的深度测量传感器。然而，激光雷达产生的大量数据可能导致数据存储和传输的高成本。虽然激光雷达数据可以表示为两种可互换的表示：3D点云和范围图像，但大多数以前的工作都集中在压缩通用3D点云上。在这项工作中，我们表明，直接压缩范围图像可以利用激光雷达扫描模式，压缩未投影的点云相比。提出了一种新的数据驱动距离图像压缩算法RIDDLE（Range Image Deep DeLta Encoding）。其核心是一个深度模型，该模型基于当前和过去扫描的上下文激光拍摄（表示为球坐标和时间的4D点云），以光栅扫描顺序预测下一个像素值。然后，可以通过熵编码来压缩预测值和原始值之间的增量。在Waymo Open Dataset和KITTI上进行了评估，与广泛使用的点云和范围图像压缩算法以及最近的深度方法相比，我们的方法在压缩率（相同失真下）方面有显着提高。
Efficient LiDAR Reflectance Compression via Scanning Serialization
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China（信息科学与技术学院，杭州师范大学）；School of Electronic Science and Engineering, Nanjing University, Nanjing, China（电子科学与工程学院，南京大学）
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（教育部机器智能与系统控制重点实验室）；School of Computer Science and Technology, Shandong University（计算机科学与技术学院，山东大学）；School of Engineering and Sustainable Development, De Montfort University（工程与可持续发展学院，德蒙福特大学）
CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting
Simon Fraser University（西蒙弗雷泽大学）；Jiangxing Intelligence Inc.（江行智能有限公司）；McGill University（麦吉尔大学）；Nankai University（南开大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Fuzhou University（福州大学）；Southeast University（东南大学）
