Depth Anything
PulseAugur coverage of Depth Anything — every cluster mentioning Depth Anything across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Video Depth Anything improves depth estimation for long videos
Researchers have developed Video Depth Anything, a novel method for consistent depth estimation in super-long videos. Building upon the Depth Anything V2 model, the new approach incorporates an efficient spatial-tempora…
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New framework enhances depth estimation models for adverse weather conditions
Researchers have developed Weather-Conditioned Depth Anything (DA-W), a new framework designed to improve monocular depth estimation models under adverse weather conditions. By disentangling weather-related style from c…
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DepthART model scales monocular depth estimation to tiny devices
Researchers have developed DepthART, a new compact model for monocular depth estimation designed for on-device deployment. This model addresses limitations in tiny models by employing a bias-resistant data sampling sche…
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Any2Full framework offers one-stage depth completion using scale-prompting adaptation
Researchers have introduced Any2Full, a novel one-stage framework for depth completion that reformulates the task as scale-prompting adaptation of a pre-trained monocular depth estimation model. This approach aims to im…
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Image generators prove to be generalist vision learners
Researchers have demonstrated that image generation models can serve as powerful generalist learners for computer vision tasks. By instruction-tuning a model called Nano Banana Pro on a mix of its original data and visi…
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Sphere-Depth benchmark evaluates monocular depth estimation for spherical cameras
Researchers have introduced Sphere-Depth, a new benchmark designed to evaluate the performance of monocular depth estimation models when applied to spherical images. This benchmark specifically addresses the challenges …