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Found 8 papers, 7 papers with code
RomniStereo: Recurrent Omnidirectional Stereo Matching
To bridge the gap between OSM and RAFT, we mainly propose an opposite adaptive weighting scheme to seamlessly transform the outputs of spherical sweeping of OSM into the required inputs for the recurrent update, thus creating a recurrent omnidirectional stereo matching (RomniStereo) algorithm.
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022
Stereo-matching is a fundamental problem in computer vision.
Dense Depth Distillation with Out-of-Distribution Simulated Images
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a trained teacher model while lacking training data in the target domain.
PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions.
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map.
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
However, this KD process can be challenging and insufficient due to the large model capacity gap between the teacher and the student.
UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation
Besides, we also designed a more effective fusion module for our fusion scheme.
Ranked #1 on
Depth Estimation
on Matterport3D
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one.
Ranked #53 on
Monocular Depth Estimation
on KITTI Eigen split
