This report summarizes the winning solutions from the RoboDepth Challenge, an academic competition focused on advancing robust monocular depth estimation under out-of-distribution (OoD) scenarios.
The challenge was based on the newly established KITTI-C and NYUDepth2-C benchmarks, which simulate realistic data corruptions across three main categories: adverse weather and lighting conditions, motion and sensor failure, and noises during data processing. Two stand-alone tracks were formed, emphasizing robust self-supervised and robust fully-supervised depth estimation, respectively.
The top-performing teams proposed novel network structures and pre-/post-processing techniques, including spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive analyses were conducted to understand the rationale behind each design.
The challenge and its winning solutions aim to lay a solid foundation for future research on robust and reliable depth estimation, which is crucial for safety-critical applications like autonomous driving and robot navigation.
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by Lingdong Kon... om arxiv.org 09-26-2024
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