FlexDepth proposes a scale-driven family of self-supervised MDE models with two-stage training and SDD decoder claiming SOTA performance and low compute on driving benchmarks.
Evaluation on the KITTI dataset [12] (Eigen split [8]) at 640×192 resolution
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Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation
FlexDepth proposes a scale-driven family of self-supervised MDE models with two-stage training and SDD decoder claiming SOTA performance and low compute on driving benchmarks.