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Abstract

Due to the limitations of current dynamic point cloud compression (DPCC) datasets, such as scarce categories, lack of detailed textures, and minimal variation in point cloud sequence movements. We present a comprehensive and diverse dataset, namely PKU-DPCC, to address the need for more categories and scene diversity for DPCC. Compared with the existing Moving Picture Experts Group (MPEG) and Audio Video Coding Standard (AVS) PCC datasets, the proposed dataset shows significant superiority in data scale, diversity, and compression difficulty. Specifically, our PKU-DPCC encompasses 50 dynamic point cloud sequences of six superclasses, and each sequence consists of 250 frames with geometry and attribute information and embodies the object or scene of a specific subclasses in the real world. Besides the diverse data content, samples in our dataset possess precise geometry details and various motion patterns, catering to a wide range of testing requirements in dynamic PCC. To construct this new dataset, we first collect numerous 3D meshes and then check the quality of each sample, resulting in 50 high-quality sequences for conversion to our final point cloud dataset. Furthermore, we conduct precise annotations for two scenarios of perceptible distortions and quality assessments on the provided point cloud data, which aims to broaden the range of its applications. To facilitate a fast algorithm performance evaluation, we select a part of representative samples constituting a subset, which has been adopted to the AVS PCC dataset. We conduct lossless and lossy compression tests on both geometry and attribute information from our subset to demonstrate the necessity of our newly constructed dataset. Experimental results reveal that the proposed dataset can become a new benchmark for evaluating and improving dynamic PCC algorithms. Our dataset is publicly available at https://openi.pcl.ac.cn/OpenDatasets/PKU-DPCC.