Volumetric video is a key enabler of immersive extended reality (XR) experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP).

Video-based Point Cloud Compression (V-PCC) standard offers state-of-the-art tools and efficiency for volumetric video compression. The V-PCC reference software, TMC2, is able to demonstrate the compression efficiency of V-PCC, but with computational complexity that is impractical for real-life applications. This paper introduces a novel open-source V-PCC encoder, named uvgVPCCenc, that brings V-PCC compression speed to a practical level. To reduce computational bottlenecks, uvgVPCCenc is implemented from the ground up in C++ with advanced multi-threading and streamlined algorithms. It also supports a flexible parameterization and different presets that facilitate its adaptation to various content and target bitrates. Our comparative evaluations show that uvgVPCCenc achieves substantial speedups over TMC2 while maintaining competitive rate-distortion-complexity tradeoff. To the best of our knowledge, uvgVPCCenc is the first open-source encoder explicitly designed for practical V-PCC compression, paving the way from theoretical research to practical volumetric video applications.

Video-based Point Cloud Compression (V-PCC) is the latest MPEG standard for visual volumetric data coding. V-PCC leverages the coding efficiency of well-established 2D video codecs by creating patches that link sub-regions of a point cloud to their 2D projections. This paper provides an in-depth analysis of the V-PCC patch generation process, which is the most compute-intensive part of the V-PCC standard. To the best of our knowledge, this is the first work to explore the patch generation tools individually and evaluate their impact on coding efficiency and complexity with different coding parameters. The main purpose of this characterization is to offer key insights into the V-PCC encoder design process and thereby foster the development of practical V-PCC encoders.

This paper presents a demonstration setup for our open-source intra encoder called uvgVPCCenc, which is optimized for real-time Video-based Point Cloud Compression (V-PCC). uvgVPCCenc achieves an average encoding speed of 26 frames per second (fps) on an Intel i7-12700 CPU when encoding volumetric video sequences with up to 185 000 points per frame. It is shown to be 700 times as fast as TMC2 reference implementation for V-PCC. Our work is the first to demonstrate real-time intra V-PCC encoding on a consumer-grade desktop computer. It indicates that even the immense computational complexity of intra V-PCC encoding can be tackled for practical applications with effective design and optimization techniques.

Point cloud compression has become a crucial factor in immersive visual media processing and streaming. This paper presents a new open dataset called UVG-VPC for the development, evaluation, and validation of MPEG Visual Volumetric Video-based Coding (V3C) technology. The dataset is distributed under its own non-commercial license. It consists of 12 point cloud test video sequences of diverse characteristics with respect to the motion, RGB texture, 3D geometry, and surface occlusion of the points. Each sequence is 10 seconds long and comprises 250 frames captured at 25 frames per second. The sequences are voxelized with a geometry precision of 9 to 12 bits, and the voxel color attributes are represented as 8-bit RGB values. The dataset also includes associated normals that make it more suitable for evaluating point cloud compression solutions. The main objective of releasing the UVG-VPC dataset is to foster the development of V3C technologies and thereby shape the future in this field.