Search Results for author:
Found 8 papers, 4 papers with code
TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models
In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy.
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem.
SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
Registering urban point clouds is a quite challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image
To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Specular reflections pose great challenges on various multimedia and computer vision tasks, e. g. , image segmentation, detection and matching.
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
