982 research outputs found
Point cloud data compression
The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic
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
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Model-based joint bit allocation between geometry and color for video-based 3D point cloud compression
The file attached to this record is the author's final peer reviewed version.In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions. Finding an optimal allocation of the total bitrate between the geometry coder and the color coder is a challenging task due to the large number of possible solutions. To solve this bit allocation problem, we first propose analytical distortion and rate models for the geometry and color information. Using these models, we formulate the joint bit allocation problem as a constrained convex optimization problem and solve it with an interior point method. Experimental results show that the rate distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity
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