Learning Depth from Monocular Videos using Direct Methods
The work proposes an end-to-end training pipeline to predict depth from monocular video using Direct Visual Odometry (DDVO) instead of separate pose prediction modules. This approach addresses scale ambiguity and improves detail recovery by combining a differentiable DDVO module with a novel depth normalization strategy, achieving performance comparable to methods trained on calibrated stereo pairs.