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Found 3 papers, 2 papers with code
Decoder Modulation for Indoor Depth Completion
Our second contribution is a novel training strategy that allows us to train on a semi-dense sensor data when the ground truth depth map is not available.
Ranked #1 on
Depth Completion
on Matterport3D
Independent Component Alignment for Multi-Task Learning
In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization.
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data
On the contrary, we propose GP$^{2}$, General-Purpose and Geometry-Preserving training scheme, and show that conventional SVDE models can learn correct shifts themselves without any post-processing, benefiting from using stereo data even in the geometry-preserving setting.
