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arXiv Computer Vision By Daikun Liu, Teng Wang, Changyin Sun

Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation

Read the original on arXiv Computer Vision →

The paper introduces HypoDepth, an event-image monocular depth estimation framework that uses a discrete Depth Hypothesis Volume (DHV) to convert depth regression into a constrained search problem. By building a lightweight 3D cost volume between DHV features and contextual features, the method performs multi-scale correlation search for stable residual optimization, enabling efficient global-to-local refinement across resolutions. Experiments on DSEC and MVSEC show state‑of‑the‑art performance, strong zero‑shot generalization, and real‑time capability on resource‑limited devices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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