mlsanityData to 7 Oct 2026

Neural Camera Models

arXiv 2208.12903 ↗First posted 27 Aug 2022cs.CVcited by 0

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Abstract (arXiv)

Modern computer vision has moved beyond the domain of internet photo collections and into the physical world, guiding camera-equipped robots and autonomous cars through unstructured environments. To enable these embodied agents to interact with real-world objects, cameras are increasingly being used as depth sensors, reconstructing the environment for a variety of downstream reasoning tasks. Machine-learning-aided depth perception, or depth estimation, predicts for each pixel in an image the distance to the imaged scene point. While impressive strides have been made in depth estimation, significant challenges remain: (1) ground truth depth labels are difficult and expensive to collect at scale, (2) camera information is typically assumed to be known, but is often unreliable and (3) restrictive camera assumptions are common, even though a great variety of camera types and lenses are used in practice. In this thesis, we focus on relaxing these assumptions, and describe contributions toward the ultimate goal of turning cameras into truly generic depth sensors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 88 of its 196 references that are in the map. First posted on arXiv 27 Aug 2022.

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88 of 196 references.

References · 88

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38 more
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