NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction (2503.18361v2)

Abstract: Recently, it has shown that priors are vital for neural implicit functions to reconstruct high-quality surfaces from multi-view RGB images. However, current priors require large-scale pre-training, and merely provide geometric clues without considering the importance of color. In this paper, we present NeRFPrior, which adopts a neural radiance field as a prior to learn signed distance fields using volume rendering for surface reconstruction. Our NeRF prior can provide both geometric and color clues, and also get trained fast under the same scene without additional data. Based on the NeRF prior, we are enabled to learn a signed distance function (SDF) by explicitly imposing a multi-view consistency constraint on each ray intersection for surface inference. Specifically, at each ray intersection, we use the density in the prior as a coarse geometry estimation, while using the color near the surface as a clue to check its visibility from another view angle. For the textureless areas where the multi-view consistency constraint does not work well, we further introduce a depth consistency loss with confidence weights to infer the SDF. Our experimental results outperform the state-of-the-art methods under the widely used benchmarks.

• The paper proposes NeRFPrior, a novel method leveraging neural radiance fields (NeRF) as a prior for efficient and accurate indoor scene reconstruction, capturing both geometry and color without large pre-training datasets.
• NeRFPrior introduces multi-view consistency constraints and a novel depth consistency loss to improve surface inference and ensure completeness, particularly in textureless areas.
• Experimental results show NeRFPrior outperforms state-of-the-art methods in accuracy and completeness, suggesting significant potential for real-time 3D mapping, AR, and robotics applications.

Overview of NeRFPrior

The paper "NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction" proposes a novel methodology for reconstructing 3D indoor scenes using neural radiance fields as priors. This approach significantly diverges from the conventional surface reconstruction methods, which often require large-scale pre-training datasets and lack color considerations in geometrical inference. The authors present NeRFPrior as a solution that leverages a neural radiance field (NeRF) to learn signed distance functions (SDFs) efficiently and accurately without the aid of additional training data.

Methodological Contributions

NeRFPrior introduces several key innovations:

1. NeRF as a Prior: Unlike traditional approaches demanding extensive pre-training, NeRFPrior utilizes NeRF for fast training within the same scene, capturing both geometry and color information. This dual-functional capacity enables better scene understanding and reconstruction fidelity.
2. Multi-view Consistency Constraint: The authors have adopted a strategy to enforce multi-view consistency at ray intersections, aiding in the inference of surface geometry. This constraint utilizes densities and colors derived from NeRF to provide visibility checks across different viewpoints, ensuring more precise surface inference.
3. Depth Consistency Loss: To tackle issues in textureless areas where traditional photometric methods may falter, a novel depth consistency loss is introduced. It calculates confidence-weighted depth for textureless surfaces to enhance surface completeness and smoothness.

Experimental Results

The experiments reveal that NeRFPrior outperforms state-of-the-art methods across recognized benchmarks, underscoring its robust reconstruction capabilities. The results demonstrate superior accuracy in capturing intricate surface details, enhanced completeness, and more realistic textures.

Implications and Future Directions

The introduction of NeRFPrior signifies a substantial step forward in indoor scene reconstruction methodologies. It circumvents some of the biggest hurdles in data-intensive approaches by relying on scene-specific training, expanding the potential for real-time applications and streamlining computational requirements. The implications for practical applications lie in real-time 3D mapping and localization, with possible extensions into augmented reality (AR) and robotics where rapid and reliable environmental interaction is necessary.

Future developments might explore further integration with dynamic scenes or broader environments outside indoor settings. Additionally, combining NeRFPrior with advancements in AI for predictive or generative tasks could widen its applicability — potentially contributing to innovative solutions in interactive media and smart environment controls.