Search Results for author:
Found 11 papers, 4 papers with code
NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks.
Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking
Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collection platform (with a head-mounted display and VR interface) that integrates both RGB and bio-inspired event cameras to capture indoor layouts in motion.
PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors
To fully exploit the priors, we have designed a shape fusion module for producing an initial complete shape from multi-modality input (i. e.,, images and point clouds), and a follow-up shape consolidation module to obtain the final complete shape by discarding unreliable points introduced by the inconsistency from diffusion priors.
Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds
In this paper, we propose the scaled-squared distance function (S$^{2}$DF), a novel implicit surface representation for modeling arbitrary surface types.
iPUNet:Iterative Cross Field Guided Point Cloud Upsampling
To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration.
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans.
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition
A non-convex weighted low-rank decomposition (NC-WLRD) model for LRMA is also proposed to reconstruct all degraded patch matrices grouped by the proposed RwM algorithm.
Dense Representative Tooth Landmark/axis Detection Network on 3D Model
Results show that our method can produce tooth landmarks with high accuracy.
Context-aware virtual adversarial training for anatomically-plausible segmentation
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.
Self-paced and self-consistent co-training for semi-supervised image segmentation
Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.
Texture Relative Superpixel Generation With Adaptive Parameters
Abstract—Superpixel generation, which is an essential step in many image processing applications, has attracted increasing attention from researchers.
