no code implementations • 13 Mar 2024 • Hongbin Xu, Weitao Chen, Feng Xiao, Baigui Sun, Wenxiong Kang
In this paper, we introduce StyleDyRF, a method that represents the 4D feature space by deforming a canonical feature volume and learns a linear style transformation matrix on the feature volume in a data-driven fashion.
no code implementations • 13 Mar 2024 • Feng Xiao, Hongbin Xu, Qiuxia Wu, Wenxiong Kang
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description.
no code implementations • 10 Oct 2023 • Hongbin Xu, Yamei Xia, Shuai Zhao, Bo Cheng
We improve the self-attention by isolating connections between irrelevant objects that makes it focus on local regions but not global regions.
no code implementations • 17 May 2023 • Weitao Chen, Hongbin Xu, Zhipeng Zhou, Yang Liu, Baigui Sun, Wenxiong Kang, Xuansong Xie
The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions.
1 code implementation • 18 Apr 2023 • Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou, Haihong Xiao, Baigui Sun, Xuansong Xie, Wenxiong Kang
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations.
1 code implementation • 17 Mar 2023 • Wanshui Gan, Ningkai Mo, Hongbin Xu, Naoto Yokoya
In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation.
no code implementations • 10 Feb 2023 • Deyun Zhang, Shijia Geng, Yang Zhou, Weilun Xu, Guodong Wei, Kai Wang, Jie Yu, Qiang Zhu, Yongkui Li, Yonghong Zhao, Xingyue Chen, Rui Zhang, Zhaoji Fu, Rongbo Zhou, Yanqi E, Sumei Fan, Qinghao Zhao, Chuandong Cheng, Nan Peng, Liang Zhang, Linlin Zheng, Jianjun Chu, Hongbin Xu, Chen Tan, Jian Liu, Huayue Tao, Tong Liu, Kangyin Chen, Chenyang Jiang, Xingpeng Liu, Shenda Hong
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
1 code implementation • ICCV 2023 • Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou, Haihong Xiao, Baigui Sun, Xuansong Xie, Wenxiong Kang
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations.
no code implementations • 24 Jul 2022 • Hongbin Xu, Weitao Chen, Yang Liu, Zhipeng Zhou, Haihong Xiao, Baigui Sun, Xuansong Xie, Wenxiong Kang
For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap.
1 code implementation • 28 May 2022 • Wanshui Gan, Hongbin Xu, Yi Huang, Shifeng Chen, Naoto Yokoya
The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task.
no code implementations • 20 Jan 2022 • Mingye Xu, Yali Wang, Zhipeng Zhou, Hongbin Xu, Yu Qiao
To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation.
1 code implementation • ICCV 2021 • Hongbin Xu, Zhipeng Zhou, Yali Wang, Wenxiong Kang, Baigui Sun, Hao Li, Yu Qiao
Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.
1 code implementation • 12 Apr 2021 • Hongbin Xu, Zhipeng Zhou, Yu Qiao, Wenxiong Kang, Qiuxia Wu
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS).