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arXiv Computer Vision By Yiming Yang, Jeremy P. Bos

Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection

Read the original on arXiv Computer Vision →

The paper investigates how snow affects LiDAR-based object detection, finding that detection accuracy actually improves with higher snow intensity. This counter‑intuitive result is explained by an increase in object point count due to snow accumulation, which outweighs the noise introduced by falling snow. The authors argue that broad weather severity bins can mislead robustness assessments unless atmospheric noise and surface accumulation effects are disentangled.

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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