19 citations · 113 across the 16 of their papers we have counts for
16 papers
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…
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…
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…
CreatureShop: Interactive 3D Character Modeling and Texturing from a Single Color Drawing
Congyi Zhang, Lei Yang, Nenglun Chen +5
Creating 3D shapes from 2D drawings is an important problem with applications in content creation for computer animation and virtual reality. We introduce a new sketch-based system…
Self-Supervised Image Representation Learning with Geometric Set Consistency
Nenglun Chen, Lei Chu, Hao Pan +2
We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency. Our intuition is that 3D geometric consistency priors such as…
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…
CurveFusion: Reconstructing Thin Structures from RGBD Sequences
Lingjie Liu, Nenglun Chen, Duygu Ceylan +3
We introduce CurveFusion, the first approach for high quality scanning of thin structures at interactive rates using a handheld RGBD camera. Thin filament-like structures are mathe…
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…
Point2Skeleton: Learning Skeletal Representations from Point Clouds
Cheng Lin, Changjian Li, Yuan Liu +3
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stri…
Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
Lei Yang, Wenxi Liu, Zhiming Cui +2
We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistenc…
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
Peng Wang, Lingjie Liu, Nenglun Chen +3
Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digita…
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…