NeRF acquisition typically requires careful choice of near planes for the different cameras or suffers from background collapse, creating floating artifacts on the edges of the captured scene. The key insight of this work is that background collapse is caused by a higher density of samples in regions near cameras. As a result of this sampling imbalance, near-camera volumes receive significantly more gradients, leading to incorrect density buildup. We propose a gradient scaling approach to counter-balance this sampling imbalance, removing the need for near planes, while preventing background collapse. Our method can be implemented in a few lines, does not induce any significant overhead, and is compatible with most NeRF implementations.

翻译：NeRF采集通常需要为不同相机仔细选择近平面，否则会导致背景坍缩，在捕获场景边缘产生浮动伪影。本工作的核心思想在于，背景坍缩是由近相机区域样本密度更高所导致的。由于这种采样不平衡，近相机体积会接收到显著更多的梯度，从而引发错误的密度堆积。我们提出了一种梯度缩放方法来抵消这种采样不平衡，在消除近平面需求的同时避免背景坍缩。我们的方法仅需几行代码即可实现，不会引入任何显著开销，并且与大多数NeRF实现兼容。