Search Results for author:
Found 5 papers, 2 papers with code
RoMo: Robust Motion Segmentation Improves Structure from Motion
There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video.
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications. However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS.
BANF: Band-limited Neural Fields for Levels of Detail Reconstruction
Largely due to their implicit nature, neural fields lack a direct mechanism for filtering, as Fourier analysis from discrete signal processing is not directly applicable to these representations.
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties.
nerf2nerf: Pairwise Registration of Neural Radiance Fields
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i. e. ICP) to operate on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained from collections of calibrated images.
