Search Results for author:
Found 18 papers, 8 papers with code
TANet: Towards Fully Automatic Tooth Arrangement
Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics.
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation
The main idea of our approach is to explore the natural relationship between multi-view correspondences and the prompt mechanism of foundational models and build bridges on it.
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.
Towards Label-free Scene Understanding by Vision Foundation Models
For nuImages and nuScenes datasets, the performance is 22. 1\% and 26. 8\% with improvements of 3. 5\% and 6. 0\%, respectively.
Zero-shot point cloud segmentation by transferring geometric primitives
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.
Self-Supervised Image Representation Learning with Geometric Set Consistency
We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency.
Towards 3D Scene Understanding by Referring Synthetic Models
Promising performance has been achieved for visual perception on the point cloud.
Referring Self-supervised Learning on 3D Point Cloud
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.
PR-Net: Preference Reasoning for Personalized Video Highlight Detection
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.
Distributed Attention for Grounded Image Captioning
We study the problem of weakly supervised grounded image captioning.
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
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.
Point2Skeleton: Learning Skeletal Representations from Point Clouds
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds.
Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
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.
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.
Unsupervised Learning of Intrinsic Structural Representation Points
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.
Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
