来自今天的爱可可AI前沿推介

[CV] Factor Fields: A Unified Framework for Neural Fields and Beyond

A Chen, Z Xu, X Wei, S Tang, H Su, A Geiger
[ETH Zurich & Adobe Research & University of California, San Diego & University of Tubingen]

因子场：用于建模和信号表示的神经场统一框架

要点:

  1. 提出因子场，一种用于建模和信号表示的新框架，将信号分解为因子的乘积；
  2. 提出CoBaFa，因子场族的一种新的表示方法，将信号分解为系数和基因子；
  3. 对 CoBaFa 在 2D 图像回归、3D SDF 重建和辐射场重建任务上进行了广泛的评估，并证明其性能比之前的方法有所提高。

一句话总结:
提出因子场，一种用于建模和信号表示的统一框架，推广了之前的神经场表征，使新模型具有更好的准确性、效率和紧凑性。

摘要：
本文提出因子场，一种用于建模和信号表示的新框架。因子场将信号分解为因子的乘积，其中每个因子都由在坐标转换的输入信号上操作的神经场或规则场表示。这种分解产生了一个统一的框架，推广了最近的几种信号表示方法，包括 NeRF、PlenOxels、EG3D、Instant-NGP 和 TensoRF。此外，该框架允许创建强大的新信号表示法，如本文提出的系数基数分解(CoBaFa)。正如实验所证明的那样，CoBaFa 在神经信号表征的三个关键目标方面比之前的快速重建方法有所改进：近似质量、紧凑性和效率。通过实验证明了与之前的快速重建方法相比，所提出表示在 2D 图像回归任务中实现了更好的图像近似质量，在重建 3D 有符号距离场时实现了更高的几何质量，在辐射场重建任务中实现了更高的紧凑性。CoBaFa表示通过在训练过程中分享不同信号的基来实现泛化，从而实现泛化任务，如用稀疏的观测值进行图像回归和少样本辐射场重建。

We present Factor Fields, a novel framework for modeling and representing signals. Factor Fields decomposes a signal into a product of factors, each of which is represented by a neural or regular field representation operating on a coordinate transformed input signal. We show that this decomposition yields a unified framework that generalizes several recent signal representations including NeRF, PlenOxels, EG3D, Instant-NGP, and TensoRF. Moreover, the framework allows for the creation of powerful new signal representations, such as the Coefficient-Basis Factorization (CoBaFa) which we propose in this paper. As evidenced by our experiments, CoBaFa leads to improvements over previous fast reconstruction methods in terms of the three critical goals in neural signal representation: approximation quality, compactness and efficiency. Experimentally, we demonstrate that our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields and higher compactness for radiance field reconstruction tasks compared to previous fast reconstruction methods. Besides, our CoBaFa representation enables generalization by sharing the basis across signals during training, enabling generalization tasks such as image regression with sparse observations and few-shot radiance field reconstruction.

论文链接：https://arxiv.org/abs/2302.01226
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