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NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction (2408.10178v2)

Published 19 Aug 2024 in cs.CV and cs.AI

Abstract: Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. Although promising, SDF-based methods often fail to capture detailed geometric structures, resulting in visible defects. By comparing SDF-based volume rendering to density-based volume rendering, we identify two main factors within the SDF-based approach that degrade surface quality: SDF-to-density representation and geometric regularization. These factors introduce challenges that hinder the optimization of the SDF field. To address these issues, we introduce NeuRodin, a novel two-stage neural surface reconstruction framework that not only achieves high-fidelity surface reconstruction but also retains the flexible optimization characteristics of density-based methods. NeuRodin incorporates innovative strategies that facilitate transformation of arbitrary topologies and reduce artifacts associated with density bias. Extensive evaluations on the Tanks and Temples and ScanNet++ datasets demonstrate the superiority of NeuRodin, showing strong reconstruction capabilities for both indoor and outdoor environments using solely posed RGB captures. Project website: https://open3dvlab.github.io/NeuRodin/

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Citations (4)
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Summary

  • The paper presents a two-stage framework that introduces local scale adaptation for SDF-to-density conversion to accurately capture complex geometry.
  • It employs a novel loss function with explicit bias correction that aligns the maximum probability with the zero level set for precise rendering.
  • Experimental results on Tanks and Temples and ScanNet++ show NeuRodin outperforms state-of-the-art models in high-fidelity neural surface reconstruction.

Essay: Analyzing the NeuRodin Framework for Advanced Neural Surface Reconstruction

The paper "NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction" introduces a novel approach to neural surface reconstruction that leverages a two-stage framework. The primary objective of the NeuRodin framework is to enhance the fidelity of 3D surface reconstructions, effectively capturing both large-scale structures and intricate details using merely posed RGB captures. This paper presents a critical analysis of the inadequacies inherent in existing SDF-based volume rendering techniques and proposes methodological innovations to address these challenges.

The key insights from the paper revolve around two major shortcomings in current SDF-based approaches: the limitations of SDF-to-density conversion and geometric regularization. In existing methods, the transition from SDF to a density field results in uniformly distributed density across level sets, which constrains the model's ability to represent arbitrary topologies. Additionally, geometric constraints enforced during optimization can limit the flexibility needed to accurately capture complex structures, leading to over-regularization and suboptimal surface representations.

Main Contributions and Methodology

  1. Local Scale Adaptation in SDF-to-Density Transformation: The paper proposes an adaptive local scale parameter for the SDF-to-density conversion, diverging from the use of a global scale. This modification facilitates the representation of any non-negative density value, allowing more precise modeling of geometric disparities across an object's surface.
  2. Explicit Bias Correction: The NeuRodin framework introduces a novel loss function aiming to align rendering geometry with the implicit surface, thereby reducing density bias. This is achieved by ensuring that the maximum probability distance aligns with the zero level set of the SDF, correcting biases without necessitating assumptions about uniform density.
  3. Two-Stage Optimization Process: The paper outlines a two-stage optimization process that alleviates the drawbacks of geometric over-regularization. The initial phase resembles density-based methods, allowing free formation of topologies. Following this, a refinement stage with explicit geometric regularization culminates in a smooth, high-quality surface reconstruction. This gradual, two-pronged approach balances flexibility and precision during optimization.

Experimental Validation and Results

The NeuRodin framework's efficacy is substantiated through rigorous testing on the Tanks and Temples and ScanNet++ datasets, demonstrating remarkable performance improvements over baseline models like COLMAP, Neuralangelo, and others. Notably, NeuRodin achieves superior F-score metrics, particularly excelling in reconstructing both indoor and outdoor scenes with intricate detailing. The paper's approach not only surpasses existing SDF-based models but also sets a new benchmark on the ScanNet++ dataset, highlighting NeuRodin's capability to deliver high-fidelity surface reconstructions using fewer parameters and computational resources compared to previous state-of-the-art methods.

Implications and Future Directions

The NeuRodin framework's contributions are poised to have significant implications in the fields of augmented reality, virtual reality, and computer vision. The ability to reliably reconstruct complex surfaces from sparse input data democratizes access to high-quality 3D modeling across a variety of application domains. Moreover, this work paves the way for future research into optimizing large-scale neural surfaces, potentially integrating advanced AI techniques such as reinforcement learning to further enhance model accuracy and resource efficiency.

Looking forward, a promising area for future exploration could involve refining the proposed local scale adaptation and bias correction techniques to handle dynamically changing environments or integrating real-time processing capabilities. Additionally, further assessment of NeuRodin's scalability and performance across diverse datasets of varying complexities would provide deeper insights into its generalization capabilities.

Overall, the NeuRodin framework represents a methodological advancement in neural surface reconstruction, addressing critical limitations of its predecessors and setting a foundation for future explorations in high-fidelity 3D modeling.

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