来自今天的爱可可AI前沿推介

[CV] RobustNeRF: Ignoring Distractors with Robust Losses

S Sabour, S Vora, D Duckworth, I Krasin, D J. Fleet, A Tagliasacchi
[Google Research]

RobustNeRF: 基于鲁棒性损失的干扰物剔除

要点:

  1. NeRF模型在处理图像采集过程中非持久干扰物时很吃力，例如移动的物体、光照变化和阴影，导致了与视图相关的伪影；
  2. RobustNeRF 使用鲁棒性估计，将干扰物建模为优化问题中的异常值，并成功地将它们从场景中移除，从而在广泛的数据集上提高了性能；
    3。 之前在 NeRF 模型中处理干扰物的方法，包括预训练的语义分割模型，将干扰物建模为每个图像的瞬时现象，以及将场景分解为静态和动态部分，但 RobustNeRF 实现起来更简单，并实现了最先进的性能。

一句话总结:
提出一种名为 RobustNeRF 的方法，使用鲁棒性估计来克服神经辐射场(NeRF)中的干扰因素问题。RobustNeRF 将训练数据中的干扰物建模为异常值，并成功将其从场景中移除，从而提高了合成和真实世界场景的性能。

Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent during image capture (moving objects, lighting variations, shadows), artifacts appear as view-dependent effects or 'floaters'. To cope with distractors, we advocate a form of robust estimation for NeRF training, modeling distractors in training data as outliers of an optimization problem. Our method successfully removes outliers from a scene and improves upon our baselines, on synthetic and real-world scenes. Our technique is simple to incorporate in modern NeRF frameworks, with few hyper-parameters. It does not assume a priori knowledge of the types of distractors, and is instead focused on the optimization problem rather than pre-processing or modeling transient objects. More results on our page this https URL.

论文链接：https://arxiv.org/abs/2302.00833
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