Search Results for author:
Found 7 papers, 4 papers with code
Nerfies: Deformable Neural Radiance Fields
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones.
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets.
PhotoShape: Photorealistic Materials for Large-Scale Shape Collections
Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance.
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images.
CamP: Camera Preconditioning for Neural Radiance Fields
We propose using a proxy problem to compute a whitening transform that eliminates the correlation between camera parameters and normalizes their effects, and we propose to use this transform as a preconditioner for the camera parameters during joint optimization.
ReconFusion: 3D Reconstruction with Diffusion Priors
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
