Block-Based Inter-Frame Prediction For Dynamic Point Cloud Compression

Abstract

In recent years, 3D point clouds have gained popularity thanks to technological advances such as the increased computational power and the availability of low-cost devices for acquisition of 3D information, like RGBD sensors. However, raw point clouds demand a large amount of data for their representation, and compression is mandatory to allow efficient transmission and storage. Inter-frame prediction is a widely used approach to achieve high compression rates in 2D video encoders, but the current literature still lacks solutions that efficiently exploit temporal redundancy for point cloud encoding. In this work, we propose a novel interframe prediction for 3D point cloud compression, which explores temporal redundancies in the 3D space. Moreover, a mode decision algorithm is also proposed to dynamically choose the best encoding mode between inter and intra prediction. The proposed method yields a bitrate reduction of 15.6% and 3.5% for geometry and luma information respectively, with no significant impact in objective quality when compared to the MPEG 3DG solution, called G-PCC.

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

“…A well-designed ME module significantly enhances coding performance. It is commonly addressed through methods such as the iterative closest points algorithm (ICP) [24,27], feature matching [28], and block matching [21,29]. Mekuria et al [24] utilize ICP to estimate block-wise motions for point cloud inter coding.…”

“…However, the estimated motions in these approaches have not been well optimized in a R-D manner [24,28]. Santos et al [29] propose an R-D optimized mode decision scheme in inter-frame prediction to improve coding performance. Additionally, Kim et al [30] propose a skeleton-based nonrigid ME scheme for the compression of dynamic human point clouds, albeit with limited generality.…”

Fractional Motion Estimation for Point Cloud Compression

“…For each block, the best matching block in a reference frame is selected according to specific matching criteria, which can be based purely on geometry, e.g., an ICP-based approach that generates rigid transforms [6], or can use a combination of geometry and color attribute information [7]. Recent work has also focused on block-based motion search speedup, including both efficient search pattern design and search window reduction [8,9,10,11].…”