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Neighbourhood-Insensitive Point Cloud Normal Estimation Network

2020-08-23 05:46:58
Zirui Wang, Victor Adrian Prisacariu

Abstract

tract: We introduce a novel self-attention-based normal estimation network that is able to focus softly on relevant points and adjust the softness by learning a temperature parameter, making it able to work naturally and effectively within a large neighbourhood range. As a result, our model outperforms all existing normal estimation algorithms by a large margin, achieving 94.1% accuracy in comparison with the previous state of the art of 91.2%, with a 25x smaller model and 12x faster inference time. We also use point-to-plane Iterative Closest Point (ICP) as an application case to show that our normal estimations lead to faster convergence than normal estimations from other methods, without manually fine-tuning neighbourhood range parameters. Code available at https://code.active.vision.

Abstract (translated)

URL

https://arxiv.org/abs/2008.09965

PDF

https://arxiv.org/pdf/2008.09965


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