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Found 5 papers, 3 papers with code
Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function
To address this RGB-TSDF distribution difference, we propose a two-stage network with a 3D RGB feature completion module that completes RGB features with meaningful values for occluded areas.
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.
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features.
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 #52 on
Monocular Depth Estimation
on KITTI Eigen split
