Fast Non-Rigid Radiance Fields from Monocularized Data

TL;DR

This paper introduces a fast, efficient method for non-rigid scene reconstruction and novel view synthesis from monocularized data, achieving rapid training and real-time rendering with high accuracy.

Contribution

It proposes a novel deformation and static module approach that significantly accelerates training and inference for 360-degree inward-facing scene synthesis from monocular data.

Findings

Converges in less than 7 minutes

Achieves real-time framerates at 1K resolution

Outperforms previous methods in visual accuracy

Abstract

The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as "monocularized" data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360{\deg} inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views. Our source code and data is available at our project page https://graphics.tu-bs.de/publications/kappel2022fast.