1 code implementation • 25 May 2024 • Qikai Wang, Rundong He, Yongshun Gong, Chunxiao Ren, Haoliang Sun, Xiaoshui Huang, Yilong Yin
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce.
1 code implementation • 23 Apr 2024 • Junjie Zhang, Tianci Hu, Xiaoshui Huang, Yongshun Gong, Dan Zeng
Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges.
no code implementations • 19 Apr 2024 • Yilong Chen, Zongyi Xu, Xiaoshui Huang, Ruicheng Zhang, Xinqi Jiang, Xinbo Gao
Furthermore, to mitigate the influence of erroneous pseudo labels obtained from sparse annotations on point cloud features, we propose a multi-modal weakly supervised network for LiDAR semantic segmentation, called MM-ScatterNet.
2 code implementations • 11 Apr 2024 • Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, Jianfei Cai
Generative models, e. g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts.
no code implementations • 19 Mar 2024 • Xianglong He, Junyi Chen, Sida Peng, Di Huang, Yangguang Li, Xiaoshui Huang, Chun Yuan, Wanli Ouyang, Tong He
To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline.
no code implementations • 17 Mar 2024 • Qianyang Wu, Ye Shi, Xiaoshui Huang, Jingyi Yu, Lan Xu, Jingya Wang
This paper addresses new methodologies to deal with the challenging task of generating dynamic Human-Object Interactions from textual descriptions (Text2HOI).
1 code implementation • 22 Feb 2024 • Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, Deng Cai, Wanli Ouyang
We project the freely available 3D segmentation annotations onto the 2D plane and leverage the corresponding 2D semantic maps as the supervision signal, significantly enhancing the semantic awareness of multi-view detectors.
1 code implementation • 2 Feb 2024 • Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, WangMeng Zuo, Junjun Jiang, Xianming Liu
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e. g., text, image, video, audio and 3D.
no code implementations • 15 Dec 2023 • Dingning Liu, Xiaomeng Dong, Renrui Zhang, Xu Luo, Peng Gao, Xiaoshui Huang, Yongshun Gong, Zhihui Wang
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.
no code implementations • 14 Dec 2023 • Zexiang Liu, Yangguang Li, Youtian Lin, Xin Yu, Sida Peng, Yan-Pei Cao, Xiaojuan Qi, Xiaoshui Huang, Ding Liang, Wanli Ouyang
Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects.
no code implementations • 11 Dec 2023 • Yue Wu, Yongzhe Yuan, Xiaolong Fan, Xiaoshui Huang, Maoguo Gong, Qiguang Miao
We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation.
no code implementations • 5 Dec 2023 • Weijie Wang, Guofeng Mei, Bin Ren, Xiaoshui Huang, Fabio Poiesi, Luc van Gool, Nicu Sebe, Bruno Lepri
The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods.
no code implementations • 3 Dec 2023 • Wentao Qu, Yuantian Shao, Lingwu Meng, Xiaoshui Huang, Liang Xiao
Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design.
no code implementations • 25 Nov 2023 • Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong
This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.
no code implementations • 11 Aug 2023 • Yongqi Huang, Peng Ye, Xiaoshui Huang, Sheng Li, Tao Chen, Tong He, Wanli Ouyang
As Vision Transformers (ViTs) are gradually surpassing CNNs in various visual tasks, one may question: if a training scheme specifically for ViTs exists that can also achieve performance improvement without increasing inference cost?
no code implementations • 19 Jun 2023 • Qinghong Sun, Yangguang Li, Zexiang Liu, Xiaoshui Huang, Fenggang Liu, Xihui Liu, Wanli Ouyang, Jing Shao
However, the quality and diversity of existing 3D object generation methods are constrained by the inadequacies of existing 3D object datasets, including issues related to text quality, the incompleteness of multi-modal data representation encompassing 2D rendered images and 3D assets, as well as the size of the dataset.
1 code implementation • NeurIPS 2023 • Zhenfei Yin, Jiong Wang, JianJian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, Jing Shao, Wanli Ouyang
To the best of our knowledge, we present one of the very first open-source endeavors in the field, LAMM, encompassing a Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark.
no code implementations • 23 May 2023 • Xiaoshui Huang, Guofeng Mei, Jian Zhang
The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies.
no code implementations • CVPR 2023 • Guofeng Mei, Hao Tang, Xiaoshui Huang, Weijie Wang, Juan Liu, Jian Zhang, Luc van Gool, Qiang Wu
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
no code implementations • 17 Dec 2022 • Yuan YAO, Yuanhan Zhang, Zhenfei Yin, Jiebo Luo, Wanli Ouyang, Xiaoshui Huang
The recent success of pre-trained 2D vision models is mostly attributable to learning from large-scale datasets.
2 code implementations • 8 Dec 2022 • Xiaoshui Huang, Zhou Huang, Sheng Li, Wentao Qu, Tong He, Yuenan Hou, Yifan Zuo, Wanli Ouyang
These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation.
no code implementations • 14 Nov 2022 • Xiaopei Wu, Yang Zhao, Liang Peng, Hua Chen, Xiaoshui Huang, Binbin Lin, Haifeng Liu, Deng Cai, Wanli Ouyang
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
no code implementations • 7 Nov 2022 • Yujiao Wu, Yaxiong Wang, Xiaoshui Huang, Fan Yang, Sai Ho Ling, Steven Weidong Su
This paper focuses on the task of survival time analysis for lung cancer.
1 code implementation • 1 Nov 2022 • Xiaoshui Huang, Wentao Qu, Yifan Zuo, Yuming Fang, Xiaowei Zhao
In this paper, we propose General Multimodal Fusion (GMF) to learn to reject the correspondence outliers by leveraging both the structure and texture information.
1 code implementation • ICCV 2023 • Tianyu Huang, Bowen Dong, Yunhan Yang, Xiaoshui Huang, Rynson W. H. Lau, Wanli Ouyang, WangMeng Zuo
To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.
Ranked #3 on Training-free 3D Point Cloud Classification on ScanObjectNN (using extra training data)
no code implementations • 29 Jul 2022 • Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang, Quan Z. Sheng, Shoujin Wang, Xiaoshui Huang, Zhenmei Yu
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.
no code implementations • 29 Dec 2021 • Guofeng Mei, Xiaoshui Huang, Litao Yu, Jian Zhang, Mohammed Bennamoun
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration.
no code implementations • 23 Nov 2021 • Xiaoshui Huang, Zongyi Xu, Guofeng Mei, Sheng Li, Jian Zhang, Yifan Zuo, Yucheng Wang
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
1 code implementation • 18 Nov 2021 • Xiaoshui Huang, Wentao Qu, Yifan Zuo, Yuming Fang, Xiaowei Zhao
In this paper, we propose a new multimodal fusion method to generate a point cloud registration descriptor by considering both structure and texture information.
Ranked #1 on Point Cloud Registration on 3DMatch Benchmark (using extra training data)
no code implementations • 12 Jun 2021 • Yujiao Wu, Jie Ma, Xiaoshui Huang, Sai Ho Ling, Steven Weidong Su
To improve the survival prediction accuracy and help prognostic decision-making in clinical practice for medical experts, we for the first time propose a multimodal deep learning method for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA.
no code implementations • 3 Mar 2021 • Xiaoshui Huang, Guofeng Mei, Jian Zhang, Rana Abbas
This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight.
no code implementations • 9 Jun 2020 • Xiaoshui Huang, Fujin Zhu, Lois Holloway, Ali Haidar
Compared with the direct combination of data imputation and causal discovery methods, our method performs generally better and can even obtain a performance gain as much as 43. 2%.
no code implementations • 30 May 2020 • Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, Wenpeng Lu
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
no code implementations • 8 May 2020 • Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang, Quanzheng Sheng, Shoujin Wang, Xiaoshui Huang, Zhemei Yu
To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.
1 code implementation • CVPR 2020 • Xiaoshui Huang, Guofeng Mei, Jian Zhang
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences.
no code implementations • 11 Mar 2019 • Xiaoshui Huang, Lixin Fan, Qiang Wu, Jian Zhang, Chun Yuan
Accurate and fast registration of cross-source 3D point clouds from different sensors is an emerged research problem in computer vision.
no code implementations • 24 Aug 2017 • Xiaoshui Huang
As the development of 3D sensors, registration of 3D data (e. g. point cloud) coming from different kind of sensor is dispensable and shows great demanding.
no code implementations • 24 Oct 2016 • Xiaoshui Huang, Jian Zhang, Qiang Wu, Lixin Fan, Chun Yuan
In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud.
no code implementations • 18 Aug 2016 • Xiaoshui Huang, Jian Zhang, Lixin Fan, Qiang Wu, Chun Yuan
We propose a systematic approach for registering cross-source point clouds.