| Title | Online Learning of a Probabilistic and Adaptive Scene Representation |
| Authors | Yan, Zike Wang, Xin Zha, Hongbin |
| Affiliation | Peking Univ, Sch EECS, Key Lab Machine Percept MOE, PKU SenseTime Machine Vis Joint Lab, Beijing, Peoples R China |
| Keywords | EFFICIENT RESOLUTION MAPS |
| Issue Date | 2021 |
| Publisher | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
| Abstract | Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene with a Bayesian nonparametric mixture model, seamlessly describing per-point occupancy status with a continuous probability density function. Instead of following the conventional data fusion paradigm, we address the problem of online learning the process how sequential point cloud data are generated from the scene geometry. An incremental and parallel inference is performed to update the parameter space in real-time. We experimentally show that the proposed representation achieves state-of-the-art accuracy with promising efficiency. The consistent probabilistic formulation assures a generative model that is adaptive to different sensor characteristics, and the model complexity can be dynamically adjusted on-the-fly according to different data scales. |
| URI | http://hdl.handle.net/20.500.11897/636844 |
| ISBN | 978-1-6654-4509-2 |
| ISSN | 1063-6919 |
| DOI | 10.1109/CVPR46437.2021.01291 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 信息科学技术学院 机器感知与智能教育部重点实验室 |
