.png){kind=icon}
返回
Inter-Frame Compression for Dynamic Point Cloud Geometry Coding
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2023.3343096.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Point cloud
compression
PCC
deep learning
neural network
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
A novel and efficient xanthenic dye–organometallic ion‐pair complex for photoinitiating polymerization一种用于光引发聚合的新型高效的黄原胶染料-有机金属离子对配合物
PREAI
分享收藏
A Volumetric Approach to Point Cloud Compression-Part II: Geometry Compression点云压缩的体积方法-第二部分: 几何压缩
PREAI
分享收藏Lösliche
trans
‐Di‐1‐alkinyl‐ und Poly‐
trans
‐ethinyl(tetraalkylphthalocyaninato)metall‐IVB‐DerivateLösliche
trans
-二-1-炔基-与聚-
trans
-乙炔基 (四烷基酞菁) 金属-ⅳ-衍生物
PREAI
分享收藏
查看更多内容