Abstract
Our objective is to leverage a differentiable radiance field \eg NeRF to reconstruct detailed 3D surfaces in addition to producing the standard novel view renderings. There have been related methods that perform such tasks, usually by utilizing a signed distance field (SDF). However, the state-of-the-art approaches still fail to correctly reconstruct the small-scale details, such as the leaves, ropes, and textile surfaces. Considering that different methods formulate and optimize the projection from SDF to radiance field with a globally constant Eikonal regularization, we improve with a ray-wise weighting factor to prioritize the rendering and zero-crossing surface fitting on top of establishing a perfect SDF. We propose to adaptively adjust the regularization on the signed distance field so that unsatisfying rendering rays won't enforce strong Eikonal regularization which is ineffective, and allow the gradients from regions with well-learned radiance to effectively back-propagated to the SDF. Consequently, balancing the two objectives in order to generate accurate and detailed surfaces. Additionally, concerning whether there is a geometric bias between the zero-crossing surface in SDF and rendering points in the radiance field, the projection becomes adjustable as well depending on different 3D locations during optimization. Our proposed \textit{RaNeuS} are extensively evaluated on both synthetic and real datasets, achieving state-of-the-art results on both novel view synthesis and geometric reconstruction.
Overview
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RaNeuS integrates a differentiable radiance field with a signed distance field (SDF) to achieve detailed 3D surface reconstruction, building on foundational concepts from Neural Radiance Fields (NeRF) and its predecessors.
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The paper introduces Ray-wise Adaptive Eikonal Regularization and Adaptive Geometric Bias Correction to address challenges in capturing fine details and reducing geometric bias in SDF-based surface reconstruction methods.
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Evaluation on synthetic and real datasets demonstrates that RaNeuS outperforms state-of-the-art methods in novel view synthesis and geometric reconstruction, achieving higher PSNR, SSIM scores, and improved Chamfer distance.
RaNeuS: Ray-adaptive Neural Surface Reconstruction
"RaNeuS: Ray-adaptive Neural Surface Reconstruction" presents an advanced approach that integrates a differentiable radiance field with a signed distance field (SDF) to achieve detailed 3D surface reconstruction, in addition to standard novel view rendering capabilities. This work builds on the foundational concepts established by Neural Radiance Fields (NeRF) and its predecessors like NeuS and HF-NeuS, but introduces novel techniques to address limitations in reconstructing small-scale details.
Technical Contributions
The authors identify and address two primary challenges in current SDF-based surface reconstruction methods: the inability to capture fine details and the geometric bias between the zero-crossing surface in SDF and radiance field rendering points. To solve these problems, the paper proposes two main innovations:
- Ray-wise Adaptive Eikonal Regularization: Traditional methods employ a globally constant Eikonal regularization, which can impede detailed surface reconstruction. The proposed method introduces a ray-wise weighting factor,
λ_r, that adjusts the strength of the Eikonal regularization based on the quality of the rendering rays. This ensures that poorly rendered rays do not impose a strong and unproductive regularization on the SDF, while well-rendered regions provide more meaningful gradient backpropagation to the SDF. - Adaptive Geometric Bias Correction: The disparity between zero-crossing surfaces in SDF and radiance field rendering points is mitigated by an adaptive factor,
λ_g. This factor adjusts the optimization process based on the geometric alignment, promoting consistency and reducing errors that arise from geometric bias.
Methodology
The approach leverages multi-view stereo (MVS) data, combining camera pose estimation, implicit field mesh initialization, and efficient mesh refinement to produce highly detailed and accurate surface reconstructions. The key components of RaNeuS are:
- Radiance Field Optimization: Following NeRF's approach, the radiance field is optimized by sampling points along camera rays and calculating color differences with ground truth images.
- Signed Distance Field (SDF) Regularization: A differentiable formulation of the SDF ensures that the resultant 3D reconstruction maintains a level of precision, facilitated by the proposed adaptive Eikonal regularization.
- Training with Hash Encoding: To enhance training efficiency, the method adopts multi-resolution hash encoding, significantly speeding up convergence without compromising on the detail and smoothness of the reconstructed surfaces.
Evaluation and Results
The paper provides extensive evaluations on both synthetic (NeRF-synthetic) and real datasets (Mip-NeRF 360 and DTU). The proposed RaNeuS outperforms state-of-the-art methods in both novel view synthesis and geometric reconstruction tasks. Key results include:
- Achieving the highest PSNR and SSIM scores on multiple scenes within the Mip-NeRF 360 dataset, indicating superior rendering quality.
- Demonstrating significant improvements in Chamfer distance on the DTU dataset, showing enhanced geometric reconstruction fidelity.
- Successfully reconstructing complex and detailed surfaces (e.g., ropes, leaves, fine textures) that existing methods struggled to capture.
Implications and Future Work
The implications of this research are substantial for fields requiring detailed 3D reconstructions from images, such as computer vision, augmented reality, and digital heritage preservation. From a theoretical perspective, this work advances the understanding of neural rendering and implicit surface representations, introducing mechanisms to resolve the balance between radiance field and SDF optimization.
Future developments could explore the applicability of ray-adaptive regularization techniques in dynamic scenes, or extend the model to reconstruct surfaces with varying topologies using Unsigned Distance Fields (UDF). Additionally, integrating more advanced neural rendering techniques and exploring different parameterizations of the SDF could further enhance the robustness and accuracy of 3D reconstructions.
In summary, RaNeuS presents a significant step forward in neural surface reconstruction, offering a detailed, efficient, and adaptive method that overcomes key limitations of previous approaches. The innovations in adaptive regularization and geometric bias correction provide a strong foundation for future advancements in this domain.
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