Search Results for author:
Found 5 papers, 4 papers with code
LocalBins: Improving Depth Estimation by Learning Local Distributions
We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways.
Ranked #34 on
Monocular Depth Estimation
on NYU-Depth V2
Self-Supervised Learning of Domain Invariant Features for Depth Estimation
Specifically, we extend self-supervised learning from traditional representation learning, which works on images from a single domain, to domain invariant representation learning, which works on images from two different domains by utilizing an image-to-image translation network.
AdaBins: Depth Estimation using Adaptive Bins
We address the problem of estimating a high quality dense depth map from a single RGB input image.
Ranked #7 on
Depth Estimation
on NYU-Depth V2
TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures
We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required.
High Quality Monocular Depth Estimation via Transfer Learning
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
Ranked #41 on
Monocular Depth Estimation
on KITTI Eigen split
