Neural field methods have seen great progress in various long-standing tasks in computer vision and computer graphics, including novel view synthesis and geometry reconstruction. As existing neural field methods try to predict some coordinate-based continuous target values, such as RGB for Neural Radiance Field (NeRF), all of these methods are regression models and are optimized by some regression loss. However, are regression models really better than classification models for neural field methods? In this work, we try to visit this very fundamental but overlooked question for neural fields from a machine learning perspective. We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks. The proposed NFC can easily transform arbitrary Neural Field Regressor (NFR) into its classification variant via employing a novel Target Encoding module and optimizing a classification loss. By encoding a continuous regression target into a high-dimensional discrete encoding, we naturally formulate a multi-label classification task. Extensive experiments demonstrate the impressive effectiveness of NFC at the nearly free extra computational costs. Moreover, NFC also shows robustness to sparse inputs, corrupted images, and dynamic scenes.

翻译：神经场方法在计算机视觉和计算机图形学中的多项长期任务（包括新视角合成和几何重建）中取得了显著进展。由于现有神经场方法试图预测基于坐标的连续目标值（如神经辐射场中的RGB值），因此这些方法均为回归模型，并通过某种回归损失进行优化。然而，对于神经场方法，回归模型是否真的优于分类模型？在本工作中，我们试图从机器学习视角重新审视这一基础但被忽视的问题。我们成功提出了一种新颖的神经场分类器框架，该框架将现有神经场方法构建为分类任务而非回归任务。通过采用新颖的目标编码模块并优化分类损失，所提出的神经场分类器可轻松将任意神经场回归器转化为其分类变体。通过将连续回归目标编码为高维离散编码，我们自然构建了一个多标签分类任务。大量实验表明，神经场分类器在以几乎免费的额外计算成本下展现出惊人的有效性。此外，神经场分类器还对稀疏输入、受损图像和动态场景表现出鲁棒性。