Search Results for author:
Found 10 papers, 3 papers with code
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling
Recovering 3D geometry of underwater scenes is challenging because of non-linear refraction of light at the water-air interface caused by the camera housing.
SofGAN: A Portrait Image Generator with Dynamic Styling
To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space.
A Neural Rendering Framework for Free-Viewpoint Relighting
We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs.
3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile.
Photo-Realistic Facial Details Synthesis from Single Image
Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis.
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image.
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching.
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes.
