29 citations · 124 across the 23 of their papers we have counts for
23 papers
LUMOS: Latent Universal Medical Priors for Segmentation
Zhuonan Liang, Wei Guo, Jie Gan +4
General vision foundation models (VFMs) have been primarily developed on natural images, and their utility for medical image segmentation is therefore often considered to depend on…
Multi-Space Alignments Towards Universal LiDAR Segmentation
Youquan Liu, Lingdong Kong, Xiaoyang Wu +5
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of…
HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes
Yichen Yao, Zimo Jiang, Yujing Sun +4
Human-centric 3D scene understanding has recently drawn increasing attention, driven by its critical impact on robotics. However, human-centric real-life scenarios are extremely di…
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Xidong Peng, Runnan Chen, Feng Qiao +6
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data. Especial…
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
Runnan Chen, Xinge Zhu, Nenglun Chen +6
Current successful methods of 3D scene perception rely on the large-scale annotated point cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a…
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Youquan Liu, Runnan Chen, Xin Li +9
Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a n…
Human-centric Scene Understanding for 3D Large-scale Scenarios
Yiteng Xu, Peishan Cong, Yichen Yao +6
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…
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Yuhang Lu, Qi Jiang, Runnan Chen +3
Zero-shot point cloud segmentation aims to make deep models capable of recognizing novel objects in point cloud that are unseen in the training phase. Recent trends favor the pipel…
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Youquan Liu, Lingdong Kong, Jun Cen +5
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a novel fram…
Towards Label-free Scene Understanding by Vision Foundation Models
Runnan Chen, Youquan Liu, Lingdong Kong +5
Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classificati…
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
Hanqi Jiang, Cheng Zeng, Runnan Chen +4
Recently, methods for neural surface representation and rendering, for example NeuS, have shown that learning neural implicit surfaces through volume rendering is becoming increasi…
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
Lingdong Kong, Youquan Liu, Xin Li +6
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception data…
Rethinking Range View Representation for LiDAR Segmentation
Lingdong Kong, Youquan Liu, Runnan Chen +6
LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range…
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Runnan Chen, Youquan Liu, Lingdong Kong +6
Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the…
Zero-shot point cloud segmentation by transferring geometric primitives
Runnan Chen, Xinge Zhu, Nenglun Chen +4
We investigate transductive zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects. The 3D geometric elements…
Towards 3D Scene Understanding by Referring Synthetic Models
Runnan Chen, Xinge Zhu, Nenglun Chen +5
Promising performance has been achieved for visual perception on the point cloud. However, the current methods typically rely on labour-extensive annotations on the scene scans. In…
PR-Net: Preference Reasoning for Personalized Video Highlight Detection
Runnan Chen, Penghao Zhou, Wenzhe Wang +4
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. Cu…
Distributed Attention for Grounded Image Captioning
Nenglun Chen, Xingjia Pan, Runnan Chen +7
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image w…
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Runnan Chen, Yuexin Ma, Nenglun Chen +4
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the curre…
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
Runnan Chen, Yuexin Ma, Lingjie Liu +4
Well-annotated medical images are costly and sometimes even impossible to acquire, hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates the rel…
Unsupervised Learning of Intrinsic Structural Representation Points
Nenglun Chen, Lingjie Liu, Zhiming Cui +4
Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for l…
Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
Runnan Chen, Yuexin Ma, Nenglun Chen +2
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis. Automatically and accurately locating these landmarks is a challenging…
Attending Category Disentangled Global Context for Image Classification
Keke Tang, Guodong Wei, Runnan Chen +3
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…