1 code implementation • ECCV 2020 • Guodong Wei, Zhiming Cui, Yumeng Liu, Nenglun Chen, Runnan Chen, Guiqing Li, Wenping Wang
Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics.
no code implementations • 2 May 2024 • Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception.
1 code implementation • 22 Mar 2024 • Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li, Ruigang Yang, Pascal Frossard, Wenguan Wang
HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities.
no code implementations • 5 Mar 2024 • Yichen Yao, Zimo Jiang, Yujing Sun, Zhencai Zhu, Xinge Zhu, Runnan Chen, Yuexin Ma
Human-centric 3D scene understanding has recently drawn increasing attention, driven by its critical impact on robotics.
no code implementations • 13 Oct 2023 • Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Tai Wang, Xinge Zhu, Yuexin Ma
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.
no code implementations • 29 Sep 2023 • Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Tongliang Liu, Wenping Wang
In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.
1 code implementation • ICCV 2023 • Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin Ma, Yikang Li, Yu Qiao, Yuenan Hou
Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase.
Ranked #2 on 3D Semantic Segmentation on SemanticKITTI (using extra training data)
1 code implementation • ICCV 2023 • Yiteng Xu, Peishan Cong, Yichen Yao, Runnan Chen, Yuenan Hou, Xinge Zhu, Xuming He, Jingyi Yu, Yuexin Ma
Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc.
no code implementations • ICCV 2023 • Yuhang Lu, Qi Jiang, Runnan Chen, Yuenan Hou, Xinge Zhu, Yuexin Ma
They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations.
2 code implementations • NeurIPS 2023 • Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception.
1 code implementation • NeurIPS 2023 • Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang
For nuImages and nuScenes datasets, the performance is 22. 1\% and 26. 8\% with improvements of 3. 5\% and 6. 0\%, respectively.
1 code implementation • 30 Mar 2023 • Hanqi Jiang, Cheng Zeng, Runnan Chen, Shuai Liang, Yinhe Han, Yichao Gao, Conglin Wang
To address this problem, we propose a neural implicit surface learning method called Depth-NeuS based on depth information optimization for multi-view reconstruction.
1 code implementation • ICCV 2023 • Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, Ziwei Liu
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
no code implementations • ICCV 2023 • Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, Ziwei Liu
We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.
Ranked #4 on 3D Semantic Segmentation on SemanticKITTI
1 code implementation • CVPR 2023 • Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang
For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.
no code implementations • 18 Oct 2022 • Runnan Chen, Xinge Zhu, Nenglun Chen, Wei Li, Yuexin Ma, Ruigang Yang, Wenping Wang
To this end, we propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives.
no code implementations • 20 Mar 2022 • Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Wenping Wang
Promising performance has been achieved for visual perception on the point cloud.
no code implementations • 29 Sep 2021 • Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Wenping Wang
In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.
no code implementations • ICCV 2021 • Runnan Chen, Penghao Zhou, Wenzhe Wang, Nenglun Chen, Pai Peng, Xing Sun, Wenping Wang
Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention.
no code implementations • 2 Aug 2021 • Nenglun Chen, Xingjia Pan, Runnan Chen, Lei Yang, Zhiwen Lin, Yuqiang Ren, Haolei Yuan, Xiaowei Guo, Feiyue Huang, Wenping Wang
We study the problem of weakly supervised grounded image captioning.
1 code implementation • 21 Jul 2021 • Runnan Chen, Yuexin Ma, Nenglun Chen, Lingjie Liu, Zhiming Cui, Yanhong Lin, Wenping Wang
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
no code implementations • 28 May 2021 • Runnan Chen, Yuexin Ma, Lingjie Liu, Nenglun Chen, Zhiming Cui, Guodong Wei, Wenping Wang
The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.
no code implementations • 1 Jan 2021 • Keke Tang, Guodong Wei, Jie Zhu, Yuexin Ma, Runnan Chen, Zhaoquan Gu, Wenping Wang
Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.
1 code implementation • CVPR 2020 • Nenglun Chen, Lingjie Liu, Zhiming Cui, Runnan Chen, Duygu Ceylan, Changhe Tu, Wenping Wang
The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures.
no code implementations • 10 Oct 2019 • Runnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, and Wenping Wang
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
2 code implementations • 23 Aug 2019 • Runnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, Wenping Wang
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
no code implementations • 17 Dec 2018 • Keke Tang, Guodong Wei, Runnan Chen, Jie Zhu, Zhaoquan Gu, Wenping Wang
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance.