1 code implementation • CVPR 2024 • Ziyue Feng, Huangying Zhan, Zheng Chen, Qingan Yan, Xiangyu Xu, Changjiang Cai, Bing Li, Qilun Zhu, Yi Xu
We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction.
no code implementations • 30 Dec 2023 • Zheng Chen, Qingan Yan, Huangying Zhan, Changjiang Cai, Xiangyu Xu, Yuzhong Huang, Weihan Wang, Ziyue Feng, Lantao Liu, Yi Xu
Through extensive experiments, we demonstrate the effectiveness of PlanarNeRF in various scenarios and remarkable improvement over existing works.
no code implementations • ICCV 2023 • Ziyue Feng, Liang Yang, Pengsheng Guo, Bing Li
Recent advances in neural reconstruction using posed image sequences have made remarkable progress.
1 code implementation • 29 Mar 2022 • Ziyue Feng, Liang Yang, Longlong Jing, HaiYan Wang, YingLi Tian, Bing Li
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
2 code implementations • 20 Sep 2021 • Ziyue Feng, Longlong Jing, Peng Yin, YingLi Tian, Bing Li
Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to predict initial depth maps.
Ranked #1 on
Depth Completion
on KITTI
1 code implementation • 25 Aug 2021 • Ziyue Feng, Yu Chen, Shitao Chen, Nanning Zheng
The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks.
no code implementations • 1 Aug 2021 • Peng Yin, Lingyun Xu, Ziyue Feng, Anton Egorov, Bing Li
Accurate localization on autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available.