1 code implementation • 29 Nov 2023 • Mutian Xu, Xingyilang Yin, Lingteng Qiu, Yang Liu, Xin Tong, Xiaoguang Han
We introduce SAMPro3D for zero-shot 3D indoor scene segmentation.
no code implementations • 28 Nov 2023 • Lingteng Qiu, GuanYing Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, Xiaoguang Han
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
no code implementations • 13 Aug 2023 • David Junhao Zhang, Mutian Xu, Chuhui Xue, Wenqing Zhang, Xiaoguang Han, Song Bai, Mike Zheng Shou
Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy.
1 code implementation • CVPR 2023 • Lingteng Qiu, GuanYing Chen, Jiapeng Zhou, Mutian Xu, Junle Wang, Xiaoguang Han
To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video.
no code implementations • CVPR 2023 • Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, GuanYing Chen, Shuguang Cui, Xiaoguang Han
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
no code implementations • CVPR 2023 • Mingye Xu, Mutian Xu, Tong He, Wanli Ouyang, Yali Wang, Xiaoguang Han, Yu Qiao
Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas.
no code implementations • 27 Sep 2022 • Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
1 code implementation • 17 Mar 2022 • Mutian Xu, Pei Chen, Haolin Liu, Xiaoguang Han
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
2 code implementations • CVPR 2021 • Mutian Xu, Runyu Ding, Hengshuang Zhao, Xiaojuan Qi
The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet.
Ranked #2 on Point Cloud Segmentation on PointCloud-C
3 code implementations • 20 Dec 2020 • Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, Yu Qiao
GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.
Ranked #1 on Point Cloud Segmentation on PointCloud-C