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Nenglun Chen

7 accepted papers

2023

Towards Label-free Scene Understanding by Vision Foundation Models

NeurIPS 2023poster

Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classification and segmentation tasks. However, the incorporation of CLIP and SAM for label-free scene understanding has yet to be e…

2022

Self-Supervised Image Representation Learning With Geometric Set Consistency

CVPR 2022poster

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 smooth regions and surface discontinuities may imply consistent semantics or object boundaries, and can act as strong cu…

Cited by 8PDFScholar
2021

PR-Net: Preference Reasoning for Personalized Video Highlight Detection

ICCV 2021poster

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. Current methods regard the user's history as holistic information to predict the user's preference but negating the inhere…

Cited by 14PDFScholar
2021

Point2Skeleton: Learning Skeletal Representations from Point Clouds

CVPR 2021poster

We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for co…

Cited by 70PDFScholar
2020

Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes

ECCV 2020poster

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-consistency formulation. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulat…

2020

TANet: Towards Fully Automatic Tooth Arrangement

ECCV 2020poster

Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics. Existing practice for specifying the target tooth arrangement involves tedious manual operations with the outcome quality depending heavily on the experience of individual specialists, leading…

Cited by 34SourcePDFScholar
2020

Unsupervised Learning of Intrinsic Structural Representation Points

CVPR 2020poster

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 learning a new structural representation in the form of 3D structure points. The 3D structure points produced by our meth…

Cited by 70PDFcodeScholar