Bilateral Guided Radiance Field Processing (2406.00448v2)

Abstract: Neural Radiance Fields (NeRF) achieves unprecedented performance in synthesizing novel view synthesis, utilizing multi-view consistency. When capturing multiple inputs, image signal processing (ISP) in modern cameras will independently enhance them, including exposure adjustment, color correction, local tone mapping, etc. While these processings greatly improve image quality, they often break the multi-view consistency assumption, leading to "floaters" in the reconstructed radiance fields. To address this concern without compromising visual aesthetics, we aim to first disentangle the enhancement by ISP at the NeRF training stage and re-apply user-desired enhancements to the reconstructed radiance fields at the finishing stage. Furthermore, to make the re-applied enhancements consistent between novel views, we need to perform imaging signal processing in 3D space (i.e. "3D ISP"). For this goal, we adopt the bilateral grid, a locally-affine model, as a generalized representation of ISP processing. Specifically, we optimize per-view 3D bilateral grids with radiance fields to approximate the effects of camera pipelines for each input view. To achieve user-adjustable 3D finishing, we propose to learn a low-rank 4D bilateral grid from a given single view edit, lifting photo enhancements to the whole 3D scene. We demonstrate our approach can boost the visual quality of novel view synthesis by effectively removing floaters and performing enhancements from user retouching. The source code and our data are available at: https://bilarfpro.github.io.

• The paper introduces a novel bilateral grid model to simulate locally-affine ISP effects, mitigating floater artifacts in multi-view neural rendering.
• It integrates a differentiable 3D bilateral grid for training and a low-rank 4D grid for translating 2D retouching to consistent 3D view synthesis.
• Experimental results show improved PSNR, SSIM, and reduced LPIPS, demonstrating enhanced visual coherence and aesthetic quality.

An Expert Analysis of "Bilateral Guided Radiance Field Processing"

This work presents a novel approach to enhancing the quality of Neural Radiance Fields (NeRFs) by addressing the inconsistencies inherent in multi-view image capture influenced by in-camera Image Signal Processing (ISP). Despite the established success of NeRF in novel view synthesis, it encounters pronounced artifacts, notably "floaters," due to variations in per-view ISP operations. This paper proposes a solution through Bilateral Guided Radiance Field Processing, introducing mechanisms to both disentangle these ISP-induced discrepancies and implement human-adjusted retouching across reconstructed 3D scenes.

Methodology and Contributions

The core contribution of this paper lies in its innovative use of the bilateral grid as a locally-affine model to simulate per-view ISP effects in a differentiable manner. The bilateral grid's edge-aware properties are leveraged to model the non-linear, local ISP operations typically performed by camera pipelines. This approach addresses two vital stages in the NeRF pipeline: training and finishing.

1. Differentiable Bilateral Grid for NeRF Training: By integrating a 3D bilateral grid, the authors offer a solution to approximate view-dependent camera enhancements. This augmentation is trained alongside NeRF parameters to approximate ISP effects postulated on each view, effectively mitigating floater artifacts and preserving multi-view consistency.
2. Low-Rank 4D Bilateral Grid for Radiance Field Finishing: To translate 2D human retouching to 3D space, the paper proposes a novel low-rank 4D bilateral grid. This grid adapts user adjustments made in familiar 2D photo editors to a comprehensive 3D space, ensuring consistency across all novel views in the reconstruction.

Experimental Results and Evaluation

The effectiveness of the proposed solution is robustly supported by experimental results. Comparisons against baseline ZipNeRF and other state-of-the-art techniques, such as GLO-based methods, HDRNeRF, and LLNeRF, indicate superior performance across several metrics. Specifically, the proposed bilateral guided approach shows enhancements in PSNR and SSIM, with diminished LPIPS scores, reflecting improved visual coherence and aesthetics. The results assert the method's capability to handle both moderate and substantial photometric variations, highlighting the resilience against artifacts like floaters.

Implications and Future Work

This paper's findings have significant practical implications, notably in scenarios requiring consistent visual aesthetics in 3D rendering from consumer multimedia devices, which often lack raw data access. The theoretical implications underscore a profound shift in tackling view-dependent processing inconsistencies, offering a pathway to more generalizable and nuanced scene synthesis in neural rendering.

Looking forward, the paper hints at potential trajectories in advancing 3D editing tools, driven by the demonstrated interface for intuitive user interaction with 3D content via 2D environments. Future work might explore further integration with existing commercial image processing software or develop even more refined models for capturing ISP operations across a diverse range of devices beyond the bilateral grid paradigm.

In conclusion, this paper delineates a sophisticated yet practical approach to overcoming NeRF's inherent shortcomings concerning ISP variations. It articulates an architectural advancement that not only promises enhanced visual synthesis but also enriches the broader narratives in computational photography and neural rendering.