Papers
Topics
Authors
Recent
Search
2000 character limit reached

NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction

Published 19 Aug 2024 in cs.CV and cs.AI | (2408.10178v2)

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/

Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5470–5479, 2022.
  2. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction. arXiv preprint arXiv:2406.06521, 2024.
  3. Recovering fine details for neural implicit surface reconstruction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4330–4339, 2023.
  4. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303–312, 1996.
  5. Improving neural implicit surfaces geometry with patch warping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6260–6269, 2022.
  6. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10786–10796, 2021.
  7. Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction. Advances in Neural Information Processing Systems, 35:3403–3416, 2022.
  8. Massively parallel multiview stereopsis by surface normal diffusion. In Proceedings of the IEEE international conference on computer vision, pages 873–881, 2015.
  9. Implicit geometric regularization for learning shapes. In Proceedings of the 37th International Conference on Machine Learning, pages 3789–3799, 2020.
  10. Nerf-det++: Incorporating semantic cues and perspective-aware depth supervision for indoor multi-view 3d detection. arXiv preprint arXiv:2402.14464, 2024.
  11. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1–13, 2017.
  12. Saliency guided subdivision for single-view mesh reconstruction. In 2020 International Conference on 3D Vision (3DV), pages 1098–1107. IEEE, 2020.
  13. Neuralangelo: High-fidelity neural surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8456–8465, 2023.
  14. Coxgraph: multi-robot collaborative, globally consistent, online dense reconstruction system. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8722–8728. IEEE, 2021.
  15. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210–7219, 2021.
  16. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021.
  17. idf-slam: End-to-end rgb-d slam with neural implicit mapping and deep feature tracking. arXiv preprint arXiv:2209.07919, 2022.
  18. Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022.
  19. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5589–5599, 2021.
  20. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
  21. Visco grids: Surface reconstruction with viscosity and coarea grids. Advances in Neural Information Processing Systems, 35:18060–18071, 2022.
  22. Permutosdf: Fast multi-view reconstruction with implicit surfaces using permutohedral lattices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8466–8475, 2023.
  23. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104–4113, 2016.
  24. Pixelwise view selection for unstructured multi-view stereo. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pages 501–518. Springer, 2016.
  25. Nd-sdf: Learning normal deflection fields for high-fidelity indoor reconstruction. arxiv preprint, 2024.
  26. High accuracy and visibility-consistent dense multiview stereo. IEEE transactions on pattern analysis and machine intelligence, 34(5):889–901, 2011.
  27. Neuris: Neural reconstruction of indoor scenes using normal priors. In European Conference on Computer Vision, pages 139–155. Springer, 2022.
  28. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689, 2021.
  29. Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3295–3306, 2023.
  30. Adaptive shells for efficient neural radiance field rendering. ACM Transactions on Graphics (TOG), 42(6):1–15, 2023.
  31. Debsdf: Delving into the details and bias of neural indoor scene reconstruction. arXiv preprint arXiv:2308.15536, 2023.
  32. Multi-scale geometric consistency guided and planar prior assisted multi-view stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4945–4963, 2022.
  33. Multi-scale geometric consistency guided multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5483–5492, 2019.
  34. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767–783, 2018.
  35. Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems, 34:4805–4815, 2021.
  36. Multiview neural surface reconstruction by disentangling geometry and appearance. Advances in Neural Information Processing Systems, 33:2492–2502, 2020.
  37. IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.
  38. Datap-sfm: Dynamic-aware tracking any point for robust dense structure from motion in the wild. arxiv preprint, 2024.
  39. Pvo: Panoptic visual odometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9579–9589, June 2023.
  40. Fedsurfgs: Scalable 3d surface gaussian splatting with federated learning for large scene reconstruction. arxiv preprint, 2024.
  41. Deflowslam: Self-supervised scene motion decomposition for dynamic dense slam. arXiv preprint arXiv:2207.08794, 2022.
  42. Scannet++: A high-fidelity dataset of 3d indoor scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12–22, 2023.
  43. Sdfstudio: A unified framework for surface reconstruction, 2022.
  44. Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction. Advances in neural information processing systems, 35:25018–25032, 2022.
  45. Visibility-aware multi-view stereo network. arXiv preprint arXiv:2008.07928, 2020.
  46. Nerf++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492, 2020.
  47. Towards unbiased volume rendering of neural implicit surfaces with geometry priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4359–4368, 2023.
  48. Human performance modeling and rendering via neural animated mesh. ACM Transactions on Graphics (TOG), 41(6):1–17, 2022.
  49. Anti-aliased neural implicit surfaces with encoding level of detail. In SIGGRAPH Asia 2023 Conference Papers, pages 1–10, 2023.
Citations (4)
View on Semantic Scholar

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.

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 5 tweets with 111 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free