Search Results for author: Numair Khan

Found 5 papers, 4 papers with code

Neural Fields in Visual Computing and Beyond

no code implementations22 Nov 2021 Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar

Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.

3D Reconstruction Image Animation +1

Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields

1 code implementation7 Jul 2021 Numair Khan, Min H. Kim, James Tompkin

We present an algorithm to estimate fast and accurate depth maps from light fields via a sparse set of depth edges and gradients.

Depth Estimation

Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

1 code implementation CVPR 2021 Numair Khan, Min H. Kim, James Tompkin

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection error from RGB supervision.

Depth Estimation

View-consistent 4D Light Field Depth Estimation

1 code implementation9 Sep 2020 Numair Khan, Min H. Kim, James Tompkin

Previous light field depth estimation methods typically estimate a depth map only for the central sub-aperture view, and struggle with view consistent estimation.

Depth Estimation

View-Consistent 4D Light Field Superpixel Segmentation

1 code implementation ICCV 2019 Numair Khan, Qian Zhang, Lucas Kasser, Henry Stone, Min H. Kim, James Tompkin

Many 4D light field processing applications rely on superpixel segmentations, for which occlusion-aware view consistency is important.


Cannot find the paper you are looking for? You can Submit a new open access paper.