23 citations · 40 across the 7 of their papers we have counts for
6 papers · 1 filter
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Noah Stier, Anurag Ranjan, Alex Colburn +4
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural…
HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion
Ziya Erkoç, Fangchang Ma, Qi Shan +2
Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise…
Generative Multiplane Images: Making a 2D GAN 3D-Aware
Xiaoming Zhao, Fangchang Ma, David Güera +3
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifi…
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
Yawar Siddiqui, Justus Thies, Fangchang Ma +3
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to tra…
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Diana Wofk, Fangchang Ma, Tien-Ju Yang +2
Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation f…
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth c…