NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction (2106.10689v3)

Abstract: We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs. Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision, easily get trapped in local minima, and therefore struggle with the reconstruction of objects with severe self-occlusion or thin structures. Meanwhile, recent neural methods for novel view synthesis, such as NeRF and its variants, use volume rendering to produce a neural scene representation with robustness of optimization, even for highly complex objects. However, extracting high-quality surfaces from this learned implicit representation is difficult because there are not sufficient surface constraints in the representation. In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation. We observe that the conventional volume rendering method causes inherent geometric errors (i.e. bias) for surface reconstruction, and therefore propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision. Experiments on the DTU dataset and the BlendedMVS dataset show that NeuS outperforms the state-of-the-arts in high-quality surface reconstruction, especially for objects and scenes with complex structures and self-occlusion.

• The paper introduces a novel hybrid approach that integrates SDF representation with a bias-free volume rendering formulation for precise surface reconstruction.
• The paper demonstrates superior performance with lower Chamfer distances and enhanced detail recovery on DTU and BlendedMVS benchmarks.
• The paper achieves robust handling of occlusions and thin structures, enabling high-fidelity 3D reconstructions for applications in augmented reality and robotics.

Overview of NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

The paper "NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction" presents a novel approach to surface reconstruction using neural implicit representations trained via volume rendering. NeuS, which stands for Neural Surfaces, demonstrates a significant advancement by leveraging the robustness of volume rendering methods to optimize Signed Distance Functions (SDFs) for accurate surface reconstruction.

Methodological Innovations

NeuS addresses the weaknesses of previous neural surface reconstruction methods such as Dense Volume Rendering (DVR) and Implicit Differentiable Renderer (IDR). These earlier methodologies often rely heavily on foreground masks and struggle with objects exhibiting self-occlusion or thin structures. On the other hand, volume rendering approaches like NeRF, while effective for novel view synthesis, are not well-suited for high-fidelity surface extraction due to insufficient surface constraints.

To tackle these challenges, NeuS introduces a hybrid approach:

1. Signed Distance Function (SDF) Representation: NeuS defines surfaces as the zero-level set of an SDF, which inherently offers precise surface definition.
2. Bias-Free Volume Rendering: The standard volume rendering technique introduces geometric errors when applied naively to SDFs. NeuS proposes a novel volume rendering formulation, ensuring unbiased reconstruction close to the first-order approximation of the geometry.

Empirical Evaluation and Results

The performance of NeuS is evaluated on two prominent datasets: the DTU dataset and the BlendedMVS dataset. The paper demonstrates that NeuS outperforms state-of-the-art methods in reconstructing complex objects, especially those with intricate details and significant occlusions.

Noteworthy Numerical Results

• Chamfer Distance: Across multiple scenes in the DTU dataset, NeuS consistently achieves lower Chamfer distance scores compared to both IDR and NeRF, indicating superior accuracy in surface reconstruction.
• Visual Quality: The qualitative analyses show that NeuS successfully reconstructs fine details and handles abrupt depth changes effectively, outperforming methods like IDR that tend to falter under these conditions.

Theoretical and Practical Implications

NeuS contributes a robust framework for multi-view reconstruction, combining the strengths of SDFs and volume rendering. The introduction of a bias-free rendering method for SDFs and the practical application of neural SDFs suggest several theoretical implications and practical applications:

1. Improved Robustness and Accuracy: By ensuring unbiased surface reconstruction, NeuS achieves higher fidelity and robustness in complex settings, paving the way for more accurate 3D modeling in computer vision and graphics.
2. Broad Applicability: The versatility of the approach makes it suitable for applications needing detailed 3D reconstructions, such as augmented reality, robotic vision, and cultural heritage preservation.

Future Directions

Future research could explore several enhancements and extensions of the NeuS framework:

• Localization and Adaptation: Modeling the variance in the probability distribution based on spatial characteristics could improve the precision of reconstructions in regions with different complexity levels.
• Handling Texture-less Regions: Refinements aimed at mitigating the degradation in performance for textureless surfaces would extend the applicability of NeuS to a broader range of materials and environments.
• Optimization Efficiency: Further optimizations to the training procedure could reduce computational overhead, making NeuS more feasible for real-time applications.

Conclusion

NeuS offers a significant improvement in the field of neural surface reconstruction. By marrying the advantages of implicit SDF representations with a novel, bias-free volume rendering formulation, NeuS achieves high accuracy and robustness, particularly in challenging scenarios involving complex occlusions and fine structural details. The empirical success on benchmark datasets highlights its potential for widespread adoption and inspires further research and development in neural implicit 3D reconstruction methodologies.