Having its outstanding flexibility for representing 3D objects realistically and naturally, Point Cloud (PC) has become a popular media format used in vast applications, such as the Augmented/Virtual Reality (AR/VR), autonomous driving, cultural e-heritage, and digital twin, for immersive service enabling. It then urgently calls for high-efficiency compression of point cloud, both lossy and losslessly.
A point cloud is a collection of non-uniformly and sparsely distributed points that are characterized using their 3D coordinates (e.g., (x,y,z)) and attributes (e.g., RGB colors, reflectances) if applicable. Unlike those well-structured pixel grids of a 2D image plane or video frame, point cloud relies on unconstrained displacement of points to freely represent arbitrary-shaped 3D objects. However, this puts obstacles on efficient coding of geometric occupancy due to the difficulty in characterizing and exploiting inter-correlation across spatially scattered points in a 3D space.
Wenjie Zhu, Yiling Xu, Dandan Ding, Mike Nilsson, and Zhan Ma, "Lossy Point Cloud Geometry Compression via Region-wise Processing," accepted by IEEE Trans. Circuits and Systems for Video Technology, July 2021.
Linyao Gao, Tingyu Fan, Jianqiang Wang, Yiling Xu, and Zhan Ma, "Point Cloud Geometry Compression via Neural Graph Sampling," accepted by IEEE ICIP, May 2021.
Wenjie Zhu, Zhan Ma, Yiling Xu, Li Li, and Zhu Li, "View-Dependent Dynamic Point Cloud Compression," IEEE Trans. Circuits and Systems for Video Technology, vol. 31, no. 2, pp. 765-781, Feb. 2021.
Jianqiang Wang, Hao Zhu, Haojie Liu and Zhan Ma, "Lossy Point Cloud Geometry Compression via End-to-End Learning," accepted by IEEE Trans. Circuits and Systems for Video Technology, Jan. 2021.
Jianqiang Wang, Dandan Ding, Zhu Li, and Zhan Ma, "Multiscale Point Cloud Geometry Compression," IEEE DCC, Snow Bird, Utah, March 2021
Qi Yang, Zhan Ma, Yiling Xu, Zhu Li and Jun Sun, "Inferring Point Cloud Quality via Graph Similarity," accepted by IEEE Trans. on Pattern Analysis and Machine Intelligence, Dec. 2020.
Qi Yang, Hao Chen, Zhan Ma, Yiling Xu, Rongjun Tang, and Jun Sun, "Predicting the Perceptual Quality of Point Cloud: A 3D-to-2D Projection-Based Exploration," accepted by IEEE Trans. Multimedia, Sept. 2020