NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo (2109.01129v3)

Abstract: In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF). Unlike existing neural network based optimization method that relies on estimated correspondences, our method directly optimizes over implicit volumes, eliminating the challenging step of matching pixels in indoor scenes. The key to our approach is to utilize the learning-based priors to guide the optimization process of NeRF. Our system firstly adapts a monocular depth network over the target scene by finetuning on its sparse SfM+MVS reconstruction from COLMAP. Then, we show that the shape-radiance ambiguity of NeRF still exists in indoor environments and propose to address the issue by employing the adapted depth priors to monitor the sampling process of volume rendering. Finally, a per-pixel confidence map acquired by error computation on the rendered image can be used to further improve the depth quality. Experiments show that our proposed framework significantly outperforms state-of-the-art methods on indoor scenes, with surprising findings presented on the effectiveness of correspondence-based optimization and NeRF-based optimization over the adapted depth priors. In addition, we show that the guided optimization scheme does not sacrifice the original synthesis capability of neural radiance fields, improving the rendering quality on both seen and novel views. Code is available at https://github.com/weiyithu/NerfingMVS.

• The paper introduces a guided optimization mechanism that integrates depth priors with NeRF to improve indoor 3D reconstruction accuracy.
• It bypasses unreliable pixel correspondence by optimizing implicit volume rendering with scene-specific depth cues.
• Experimental results on the ScanNet dataset demonstrate significant gains in depth estimation and rendering performance over state-of-the-art methods.

Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

The paper "NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo" introduces an innovative approach to multi-view depth estimation, leveraging neural radiance fields (NeRF) to address the challenges of reconstruction in indoor environments. The core contribution lies in integrating learning-based depth priors with NeRF to guide the optimization process, leading to improved geometric accuracy and rendering performance.

Methodology

Unlike traditional multi-view stereo (MVS) methods that struggle with poorly textured areas and challenging indoor environments, this research proposes a system that bypasses pixel correspondence matching. Instead, it employs direct optimization over implicit volumes within NeRF. This approach incorporates scene-specific depth priors, which are derived from a monocular depth network finetuned on sparse Structure from Motion (SfM) and Multi-view Stereo (MVS) reconstructions.

The innovation lies in addressing the shape-radiance ambiguity inherent in NeRF, which typically hampers accurate depth estimation in texture-less indoor scenes. By employing a guided optimization mechanism, the paper proposes to confine sample ranges during volume rendering based on the adapted depth priors. This procedure ensures that the radiance fields are better aligned with accurate geometry.

Experimental Results

Experiments conducted on the ScanNet dataset demonstrate significant improvements in depth estimation over existing state-of-the-art methods. Quantitatively, the proposed method achieves superior performance, evidenced by enhanced metrics across multiple evaluation criteria such as absolute relative error and RMSE.

Additionally, the paper reveals a notable finding regarding correspondence-based optimization. Traditional approaches relying on correspondence estimation often degrade depth quality due to unreliable flow computation. In contrast, the proposed method successfully integrates depth priors into the NeRF framework, avoiding the pitfalls of correspondence estimation.

The guided optimization scheme also positively impacts the rendering quality of NeRF, both in seen and novel views. This suggests that incorporating conventional non-learning reconstruction techniques can enhance the synthesis quality of NeRF-based methods.

Implications and Future Work

The integration of learning-based priors into the NeRF optimization framework has significant implications for both practical applications and theoretical understanding of 3D reconstruction. Practically, it enables robust depth estimation in indoor settings, enhancing applications in robotics, virtual reality, and 3D modeling. Theoretically, it provides insights into managing the shape-radiance ambiguity within neural implicit representations, potentially guiding future research in NeRF adaptations and optimizations.

Future directions could focus on optimizing the computational efficiency of the proposed approach, potentially through advanced sampling strategies or network architectures. There is also potential for extending this framework to non-rigid or dynamic scenes, opening avenues for enhanced visual effect applications based on improved 3D geometry.

In conclusion, the paper presents a compelling advancement in leveraging NeRF for multi-view stereo tasks, with a focus on indoor environments. The guided optimization approach showcases how integrating neural networks with conventional computer vision techniques can significantly enhance the performance and applicability of depth estimation systems.