论文标题
动态点云几何编码的框架间压缩
Inter-Frame Compression for Dynamic Point Cloud Geometry Coding
论文作者
论文摘要
有效的点云压缩对于虚拟和混合现实，自动驾驶和文化遗产等应用至关重要。本文提出了一种基于深度学习的框架间编码方案，用于动态点云几何压缩。我们提出了一种有损的几何压缩方案，该方案通过使用新的特征空间间预测网络，使用先前的框架来预测当前帧的潜在表示。所提出的网络利用稀疏的卷积使用层次多尺度3D特征学习来使用上一个帧编码当前帧。提出的方法在特征域中引入了一个新型的预测网络，以绘制上一个帧的潜在表示为当前帧的坐标，以预测当前帧的特征嵌入。该框架通过使用学习的概率分解熵模型来压缩预测特征的残差和实际特征。在接收器，解码器层次结构通过逐步重新嵌入功能嵌入来重建当前框架。将所提出的框架与最新的基于视频的点云压缩（V-PCC）和基于几何的点云压缩（G-PCC）方案进行了比较。 The proposed method achieves more than 88% BD-Rate (Bjontegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC。在MPEG工作组中，对这些显着的性能增长进行了交叉检查和验证。
Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame's feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjontegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group.
