167 citations · 187 across the 6 of their papers we have counts for
10 papers
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Zhaoshuo Li, Thomas Müller, Alex Evans +4
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed…
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey
Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the…
Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild
Chaoyang Wang, Chen-Hsuan Lin, Simon Lucey
The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem. Classical NRSfM app…
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell +4
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos. Our approach combines the best of multi-view geometric and data-driven methods for 3D recons…
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
Chen-Hsuan Lin, Ersin Yumer, Oliver Wang +2
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we p…
Semantic Photometric Bundle Adjustment on Natural Sequences
Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin +2
The problem of obtaining dense reconstruction of an object in a natural sequence of images has been long studied in computer vision. Classically this problem has been solved throug…