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Neuralangelo: High-Fidelity Neural Surface Reconstruction (2306.03092v2)

Published 5 Jun 2023 in cs.CV

Abstract: Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.

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Citations (283)
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Summary

  • The paper introduces Neuralangelo, which significantly enhances neural surface reconstruction fidelity through multi-resolution hash grids and numerical gradient computation.
  • It employs a coarse-to-fine optimization strategy to accurately recover surface details from standard RGB images without relying on auxiliary data.
  • Experimental results on DTU and Tanks and Temples benchmarks show improved accuracy, lower Chamfer distances, and higher PSNR compared to previous methods.

High-Fidelity Neural Surface Reconstruction with Neuralangelo

The paper "Neuralangelo: High-Fidelity Neural Surface Reconstruction" addresses persistent challenges in the field of neural surface reconstruction by presenting an innovative framework for generating detailed 3D surfaces from RGB images. This approach significantly advances the fidelity of surface representations acquired from monocular image captures without auxiliary input data such as segmentation or depth, positioning it as a robust solution for real-world scene reconstruction requirements.

Overview of Neuralangelo

Neuralangelo leverages the representational power of multi-resolution 3D hash grids combined with neural surface rendering. Traditional neural surface reconstruction techniques significantly improve over classical multi-view stereo algorithms, which often struggle with regions characterized by homogeneous colors or repetitive patterns. By utilizing multi-layer perceptrons (MLPs) to encode scenes as implicit functions, previous methods offer smooth and continuous surface representations. However, they fall short in scaling fidelity proportionate to the MLP's capacity.

Neuralangelo introduces critical methodologies to enhance surface reconstruction:

  1. Numerical Gradient Computation: The adoption of numerical gradients allows for higher-order derivative calculations, enabling non-local smoothing that improves optimization stability across grid boundaries. This method counters the locality limitation of traditional analytical gradient-based approaches.
  2. Coarse-to-Fine Optimization: By progressively optimizing hash grids from coarse to finer resolutions, Neuralangelo ensures that the structure is recovered incrementally at varying levels of detail, enhancing the capability to capture fine-grained features.

Experimental Results

Comprehensive experimentation on standard datasets like DTU and Tanks and Temples showcases Neuralangelo's superiority in both surface reconstruction accuracy and view synthesis quality. On the DTU benchmark, Neuralangelo achieves the lowest average Chamfer distance and highest PSNR compared to existing methods such as NeuS, VolSDF, and their derivatives. Furthermore, its performance on large-scale scenes from the Tanks and Temples dataset validates its applicability to complex indoor and outdoor environments, demonstrating an ability to capture intricate details that competitors miss.

Implications and Future Directions

Neuralangelo's contributions extend well beyond incremental improvements in surface fidelity. By obviating the need for auxiliary data, this framework democratizes high-quality 3D scene reconstruction, making it accessible through commonplace consumer devices equipped with standard RGB cameras. The capability to create rich digital twins of real-world environments from video captures opens avenues in fields ranging from augmented reality to autonomous navigation.

From a theoretical standpoint, Neuralangelo's use of multi-resolution hash encodings combined with innovative numerical gradient strategies sets a precedent for future research in surface representation learning. While the paper outlines robust methods for handling surface smoothness and detail via curvature regularization, further investigations might focus on enhancing computational efficiency. Strategies that enable faster convergence without loss of detail would greatly benefit practical applications requiring rapid deployment.

In conclusion, Neuralangelo marks a substantial step forward in neural surface reconstruction, bridging current gaps in fidelity and operational usability. Its methodologies provide a foundational model that encourages exploration and refinement within AI-driven reconstruction paradigms. Future work could also explore extending these approaches to handle reflective and translucent materials, thus expanding the framework's versatility in varied visual environments.

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