标题： AdaDPCC：动态点云压缩的自适应速率控制和速率失真复杂度优化 显示英文标题

标题： AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression

摘要： 动态点云压缩（DPCC）在自动驾驶和AR/VR等应用中至关重要。 当前的压缩方法在复杂度管理和码率控制方面面临挑战。 本文介绍了一种新的动态编码框架，支持可变码率和计算复杂度。 我们的方法包括一个可裁剪的框架，具有多个编码路径，允许在单一模型内进行高效的率失真复杂度优化（RDCO）。 为了解决帧间预测中的数据稀疏性问题，我们提出了一种从粗到细的运动估计和补偿模块，该模块分解几何信息的同时扩展了感知范围。 此外，我们提出了一种精确的码率控制模块，能够根据内容自适应地通过各种编码路径导航点云帧，以满足目标码率。 实验结果表明，与最先进的方法相比，我们的方法平均BD-Rate降低了5.81%，BD-PSNR提高了0.42 dB，同时保持平均码率误差在0.40%。 此外，平均编码时间相比D-DPCC减少了高达44.6%，突显了其在实时和码率受限的DPCC场景中的效率。 我们的代码可在https://git.openi.org.cn/OpenPointCloud/Ada_DPCC获取。 显示英文摘要

摘要： Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational complexities. Our approach includes a slimmable framework with multiple coding routes, allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a single model. To address data sparsity in inter-frame prediction, we propose the coarse-to-fine motion estimation and compensation module that deconstructs geometric information while expanding the perceptive field. Additionally, we propose a precise rate control module that content-adaptively navigates point cloud frames through various coding routes to meet target bitrates. The experimental results demonstrate that our approach reduces the average BD-Rate by 5.81% and improves the BD-PSNR by 0.42 dB compared to the state-of-the-art method, while keeping the average bitrate error at 0.40%. Moreover, the average coding time is reduced by up to 44.6% compared to D-DPCC, underscoring its efficiency in real-time and bitrate-constrained DPCC scenarios. Our code is available at https://git.openi.org.cn/OpenPointCloud/Ada_DPCC.