W3.2
LEARNING NEURAL VOLUMETRIC FIELD FOR POINT CLOUD GEOMETRY COMPRESSION
Yueyu Hu, Yao Wang, New York University, United States of America
Session:
Point Cloud and Mesh Coding
Track:
Visual data coding and processing
Location:
Almaden Ballroom
Presentation Time:
Wed, 7 Dec, 16:48 - 17:06 PST (UTC -7)
Session Chair:
Jörn Ostermann, Leibniz Universität Hannover, Germany
Presentation
Discussion
Resources
No resources available.
Session W3
W3.1: ATTRIBUTE-AWARE PARTITIONING FOR GRAPH-BASED POINT CLOUD ATTRIBUTE CODING
Thibaut Meyer, Maria Meyer, Dominik Mehlem, Christian Rohlfing, RWTH Aachen University, Germany
W3.2: LEARNING NEURAL VOLUMETRIC FIELD FOR POINT CLOUD GEOMETRY COMPRESSION
Yueyu Hu, Yao Wang, New York University, United States of America
W3.3: BOUNDARY-PRESERVED GEOMETRY VIDEO FOR DYNAMIC MESH CODING
Chao Huang, Xiang Zhang, Jun Tian, Xiaozhong Xu, Shan Liu, Tencent, United States of America
W3.4: Motion estimation and filtered prediction for dynamic point cloud attribute compression
Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, University of Southern California, Los Angeles CA. 90089 USA, United States of America; Keisuke Nonaka, KDDI Research, Inc., Japan, Japan
W3.5: LOGISTIC REGRESSION GUIDED CODING OF SINGLE CHILD MODE FOR POINT CLOUD GEOMETRY COMPRESSION
Zhecheng Wang, Shuai Wan, Lei Wei, Northwestern Polytechnical University, China