This AI paper introduces a 3D diffusion-based approach for casual NeRF captures, improving artifacts and enhancing scene geometry using local 3D priors and a novel loss function

Neural Radiance Fields (NeRFs) captured casually are often of lesser quality than most catches displayed in NeRF articles. The eventual goal of a typical user (for example, a hobbyist) who captures a NeRFs is frequently to create a fly-through route from a quite different set of views than the first obtained photos. This significant viewpoint shift between the training and rendering views often shows incorrect geometry and floater artifacts, as seen in Fig. 1a. It is standard practice in programs like Polycam1 and Luma2 to instruct users to draw three circles at three different heights while gazing inward at the item of interest. This technique addresses these artifacts by instructing or encouraging users to record a picture more.  

However, these capture procedures can be time-consuming, and users might need to pay more attention to complicated capture instructions to produce an artifact-free capture. Creating techniques that enable improved out-of-distribution NeRF renderings is another method for removing NeRF artifacts. The optimization of camera poses to address noisy camera poses, per-image appearance embeddings to handle variations in exposure, or resilient loss functions to manage transient occluders have been examined in earlier research as potential methods of minimizing artifacts. Even though these and other methodologies outperform conventional benchmarks, most standards rely on measuring picture quality at held-out frames from the training sequence, which is frequently not indicative of visual quality from new views. 

Figure 1c demonstrates how the Nerfacto approach deteriorates as the novel view is magnified. In this study, researchers from Google Research and UCB suggest both (1) a unique technique for restoring accidentally acquired NeRFs and (2) a fresh approach to judging a NeRF’s quality that more accurately represents rendered picture quality from unusual angles. Two films will be recorded as part of their suggested assessment protocol: one for training a NeRF and the other for novel-view evaluation (Fig. 1b). They can calculate a set of metrics on visible regions where they anticipate the scene to have been properly recorded in the training sequence using the pictures from the second capture as ground-truth (as well as depth and normals retrieved from a reconstruction on all frames). 

They record a new dataset with 12 scenes, each with two camera sequences, for training and assessment while adhering to this evaluation process. They also suggest Nerfbusters, a technique that aims to enhance surface coherence, eliminate floaters, and clear up foggy artifacts in routine NeRF recordings. Their approach employs a diffusion network trained on synthetic 3D data to acquire a local 3D geometric prior, and it leverages this before supporting realistic geometry during NeRF optimization. Local geometry is less complicated, more category-independent, and reproducible than global 3D priors, making it appropriate for random scenes and smaller-scale networks (a 28 Mb U-Net effectively simulates the distribution of all feasible surface patches). 

Given this data-driven, local 3D prior, they use a novel unconditional Density Score Distillation Sampling (DSDS) loss to regularize the NeRF. They find that this technique removes floaters and makes the scene geometry crisper. To their knowledge, they are the first to demonstrate that a learned local 3D prior can improve NeRFs. Empirically, they show that Nerfbusters achieves state-of-the-art performance for casual captures compared to other geometry regularizers. They implement their evaluation procedure and Nerfbusters method in the open-source Nerfstudio repository. The code and data can be found on GitHub.

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