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The robodrive challenge: Drive anytime anywhere in any condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …
paramount for the safe deployment of vehicles. Challenges such as adverse weather …
The third monocular depth estimation challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation
Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging …
Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging …
The second monocular depth estimation challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …
Challenge (MDEC). This edition was open to methods using any form of supervision …
The robodepth challenge: Methods and advancements towards robust depth estimation
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
We introduce Stereo Anywhere, a novel stereo-matching framework that combines
geometric constraints with robust priors from monocular depth Vision Foundation Models …
geometric constraints with robust priors from monocular depth Vision Foundation Models …
The second monocular depth estimation challenge: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …
Challenge (MDEC). This edition was open to methods using any form of supervision …
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …
Challenge (MDEC). This edition was open to methods using any form of supervision …