邻域不敏感点云法向估计网络
Neighbourhood-Insensitive Point Cloud Normal Estimation Network
摘要：
我们引入了一种新的基于自注意力的正常估计网络，该网络能够柔和地关注相关点，并通过学习温度参数来调整柔和度，使其能够在较大的邻域范围内自然有效地工作。因此，我们的模型远远优于所有现有的正常估计算法，与先前的91.2%的最新技术相比，准确率达到94.1%，模型小25倍，推理时间快12倍。我们还使用点到面迭代最近点（ICP）作为应用案例，表明我们的正常估计比其他方法的正常估计更快的收敛，而无需手动微调邻域范围参数。代码可在https://code.active.vision获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
