Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction
The sparse LiDAR point clouds become more and more popular in various applications, e.g., the autonomous driving. However, for this type of data, there exists much under-explored space in the corresponding compression framework proposed by MPEG, i.e., geometry-based point cloud compression (G-PCC). In G-PCC, only the distance-based similarity is considered in the intra prediction for the attribute compression. In this paper, we propose a normal-based intra prediction scheme, which provides a more efficient lossless attribute compression by introducing the normals of point clouds. The angle between normals is used to further explore accurate local similarity, which optimizes the selection of predictors. We implement our method into the G-PCC reference software. Experimental results over LiDAR acquired datasets demonstrate that our proposed method is able to deliver better compression performance than the G-PCC anchor, with $2.1\%$ gains on average for lossless attribute coding.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeAutonomous DrivingSimilar Papers 제목 키워드 기반
Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper,…
AttributeAutonomous DrivingHierarchical Attention Networks for Lossless Point Cloud Attribute Compression
In this paper, we propose a deep hierarchical attention context model for lossless attribute compression of point clouds, leveraging a multi-resolution spatial structure and residual learning. A simple and effective Leve…
AttributeDeep probabilistic model for lossless scalable point cloud attribute compression
In recent years, several point cloud geometry compression methods that utilize advanced deep learning techniques have been proposed, but there are limited works on attribute compression, especially lossless compression. …
AttributeDALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is under-explored.…
Point CloudsEfficient and Generic Point Model for Lossless Point Cloud Attribute Compression
The past several years have witnessed the emergence of learned point cloud compression (PCC) techniques. However, current learning-based lossless point cloud attribute compression (PCAC) methods either suffer from high c…
2kAttribute