Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression

As being one of the main representation formats of 3D real world and well-suited for virtual reality and augmented reality applications, point clouds have gained a lot of popularity. In order to reduce the huge amount of data, a considerable amount of research on point cloud compression has been done. However, given a target bit rate, how to properly choose the color and geometry quantization parameters for compressing point clouds is still an open issue. In this paper, we propose a rate-distortion model based quantization parameter selection scheme for bit rate constrained point cloud compression. Firstly, to overcome the measurement uncertainty in evaluating the distortion of the point clouds, we propose a unified model to combine the geometry distortion and color distortion. In this model, we take into account the correlation between geometry and color variables of point clouds and derive a dimensionless quantity to represent the overall quality degradation. Then, we derive the relationships of overall distortion and bit rate with the quantization parameters. Finally, we formulate the bit rate constrained point cloud compression as a constrained minimization problem using the derived polynomial models and deduce the solution via an iterative numerical method. Experimental results show that the proposed algorithm can achieve optimal decoded point cloud quality at various target bit rates, and substantially outperform the video-rate-distortion model based point cloud compression scheme.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technolog

Aggressive saliency-aware point cloud compression

The increasing demand for accurate representations of 3D scenes, combined with immersive technologies has led point clouds to extensive popularity. However, quality point clouds require a large amount of data and therefore the need for compression methods is imperative. In this paper, we present a novel, geometry-based, end-to-end compression scheme, that combines information on the geometrical features of the point cloud and the user's position, achieving remarkable results for aggressive compression schemes demanding very small bit rates. After separating visible and non-visible points, four saliency maps are calculated, utilizing the point cloud's geometry and distance from the user, the visibility information, and the user's focus point. A combination of these maps results in a final saliency map, indicating the overall significance of each point and therefore quantizing different regions with a different number of bits during the encoding process. The decoder reconstructs the point cloud making use of delta coordinates and solving a sparse linear system. Evaluation studies and comparisons with the geometry-based point cloud compression (G-PCC) algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety of point clouds, demonstrate that the proposed method achieves significantly better results for small bit rates

Enhancement layer inter frame coding for 3D dynamic point clouds

In recent years, Virtual Reality (VR) and Augmented Reality (AR) applications have seen a drastic increase in commercial popularity. Different representations have been used to create 3D reconstructions for AR and VR. Point clouds are one such representation that are characterized by their simplicity and versatil

Moxel DAGs: Connecting Material Information to High Resolution Sparse Voxel DAGs

As time goes on, the demand for higher resolution and more visually rich images only increases. Unfortunately, creating these more realistic computer graphics is pushing our computational resources to their limits. In realistic rendering, one of the common ways 3D objects are represented is as volumetric elements called voxels. Traditionally, voxel data structures are known for their high memory requirements. One of the standard ways these requirements are minimized is by storing the voxels in a sparse voxel octree (SVO). Very recently, a method called High Resolution Sparse Voxel DAGs was presented that can store binary voxel data orders of magnitudes more efficiently than SVOs. This memory efficiency is achieved by converting the tree into a directed acyclic graph (DAG). The method was also shown to have competitive rendering performance to recent GPU ray tracers. Unfortunately, it does not support storing collections of rendering attributes, commonly called materials. These represent a given object\u27s reflectance properties, and are necessary for calculating its perceived color. We present a method for connecting material information to High Resolution Sparse Voxel DAGs for mid-level scenes, with multiple meshes, and several different materials. This is achieved using an extended Sparse Voxel DAG, called a Moxel DAG, and an external data structure for holding the material information, we call a Moxel Table. Our method is much more memory efficient than traditional SVOs, and only increases in efficiency in comparison when at higher resolutions. Because it stores the equivalent information as SVOs, it achieves the exact same visual quality at the same resolutions