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UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene (2310.13263v1)

Published 20 Oct 2023 in cs.CV

Abstract: Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant limitations. To address these challenges, in this paper, we propose a novel neural rendering system called UE4-NeRF, specifically designed for real-time rendering of large-scale scenes. We partitioned each large scene into different sub-NeRFs. In order to represent the partitioned independent scene, we initialize polygonal meshes by constructing multiple regular octahedra within the scene and the vertices of the polygonal faces are continuously optimized during the training process. Drawing inspiration from Level of Detail (LOD) techniques, we trained meshes of varying levels of detail for different observation levels. Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4), achieving real-time rendering of large-scale scenes at 4K resolution with a frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing in subsequent stages. Furthermore, through experiments, we have demonstrated that our method achieves rendering quality comparable to state-of-the-art approaches. Project page: https://jamchaos.github.io/UE4-NeRF/.

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References (48)
  1. Voxel-based morphometry—the methods. Neuroimage, 11(6):805–821, 2000.
  2. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021.
  3. 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.
  4. A survey of surface reconstruction from point clouds. In Computer graphics forum, volume 36, pages 301–329. Wiley Online Library, 2017.
  5. Polygon mesh processing. CRC press, 2010.
  6. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. arXiv preprint arXiv:2208.00277, 2022.
  7. Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12475–12485, 2020.
  8. James H Clark. Hierarchical geometric models for visible surface algorithms. Communications of the ACM, 19(10):547–554, 1976.
  9. High-quality streamable free-viewpoint video. ACM Transactions on Graphics (ToG), 34(4):1–13, 2015.
  10. Depth-supervised nerf: Fewer views and faster training for free. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12882–12891, 2022.
  11. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501–5510, 2022.
  12. Accurate, dense, and robust multi-view stereopsis (pmvs). In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, 2007.
  13. Object-centric neural scene rendering. arXiv preprint arXiv:2012.08503, 2020.
  14. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12):4338–4364, 2020.
  15. Multiple view geometry in computer vision. Cambridge university press, 2003.
  16. Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366, 1989.
  17. Real shading in unreal engine 4. Proc. Physically Based Shading Theory Practice, 4(3):1, 2013.
  18. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019.
  19. Control-nerf: Editable feature volumes for scene rendering and manipulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4340–4350, 2023.
  20. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651–15663, 2020.
  21. Level of detail for 3D graphics. Morgan Kaufmann, 2003.
  22. 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.
  23. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021.
  24. Claus Müller. Spherical harmonics, volume 17. Springer, 2006.
  25. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4):1–15, 2022.
  26. Jae-Ho Nah. Quicketc2: Fast etc2 texture compression using luma differences. ACM Transactions on Graphics (TOG), 39(6):1–10, 2020.
  27. Donerf: Towards real-time rendering of compact neural radiance fields using depth oracle networks. In Computer Graphics Forum, volume 40, pages 45–59. Wiley Online Library, 2021.
  28. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453–11464, 2021.
  29. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865–5874, 2021.
  30. Unrealcv: Connecting computer vision to unreal engine. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pages 909–916. Springer, 2016.
  31. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14335–14345, 2021.
  32. Nerf-slam: Real-time dense monocular slam with neural radiance fields. arXiv preprint arXiv:2210.13641, 2022.
  33. Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results. Neural networks, 11(1):15–37, 1998.
  34. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104–4113, 2016.
  35. Light field networks: Neural scene representations with single-evaluation rendering. Advances in Neural Information Processing Systems, 34:19313–19325, 2021.
  36. Block-nerf: Scalable large scene neural view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8248–8258, 2022.
  37. Nerfstudio: A modular framework for neural radiance field development. arXiv preprint arXiv:2302.04264, 2023.
  38. Bundle adjustment—a modern synthesis. In Vision Algorithms: Theory and Practice: International Workshop on Vision Algorithms Corfu, Greece, September 21–22, 1999 Proceedings, pages 298–372. Springer, 2000.
  39. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12922–12931, 2022.
  40. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
  41. Alpha dithering to correct low-opacity 8 bit compositing errors. Lawrence Livermore National Laboratory Technical Report UCRL-ID-153185, 2003.
  42. Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII, pages 106–122. Springer, 2022.
  43. Learning object-compositional neural radiance field for editable scene rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13779–13788, 2021.
  44. Plenoctrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752–5761, 2021.
  45. Nerf++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492, 2020.
  46. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018.
  47. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881–2890, 2017.
  48. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223–2232, 2017.
Citations (14)
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Summary

  • The paper introduces a method that partitions large scenes into sub-NeRFs and optimizes them with polygonal meshes for efficient, high-fidelity rendering.
  • It incorporates a multi-level detail strategy that dynamically adjusts mesh complexity to balance rendering performance and visual quality.
  • Integrating closely with Unreal Engine 4, UE4-NeRF achieves real-time 4K rendering at up to 43 FPS, expanding applications in gaming and virtual reality.

Real-Time Rendering of Large-Scale Scenes with UE4-NeRF

The paper "UE4-NeRF: Neural Radiance Field for Real-Time Rendering of Large-Scale Scene" introduces an innovative approach to address the challenge of rendering large-scale scenes in real-time with high fidelity. This research leverages Neural Radiance Fields (NeRF), a framework for 3D reconstruction and novel view synthesis from 2D images, to achieve real-time interactive rendering in large environments using Unreal Engine 4 (UE4).

Overview of UE4-NeRF

Traditional NeRF systems, while effective at rendering and reconstructing 3D scenes, struggle with real-time performance and scalability to larger scene sizes due to computational complexity and storage requirements. The UE4-NeRF approach addresses these limitations by partitioning large scenes into sub-NeRFs and representing them using polygonal meshes. By implementing multiple levels of detail (LOD) and integrating closely with the UE4 rasterization pipeline, UE4-NeRF facilitates real-time rendering at 4K resolution, reaching frame rates up to 43 FPS.

Methodology and Key Contributions

UE4-NeRF's methodology incorporates several significant innovations:

  • Scene Partitioning and Mesh Representation: The authors partition large-scale scenes into smaller, manageable sub-scenes, which are individually represented using polygonal meshes. These meshes are initialized with regular octahedra and optimized iteratively during training to ensure minimal computational overhead while maintaining visual fidelity.
  • Use of Multi-Level Detail: By employing a novel LOD approach, UE4-NeRF dynamically adjusts mesh complexity according to the observation distance, balancing rendering speed and visual quality. This mechanism ensures efficient use of computational resources and supports interactive visualizations.
  • Integration with UE4: The integration with Unreal Engine 4 not only enhances rendering performance but also unlocks additional functionalities such as scene editing and object manipulation. This inclusion makes UE4-NeRF versatile for applications in gaming, virtual reality, and other interactive digital environments.

The experimental results highlight that UE4-NeRF matches the rendering quality of leading state-of-the-art methods while achieving the advantage of real-time performance, previously unattainable for large and complex terrains.

Implications and Future Directions

The implications of UE4-NeRF are substantial for both practical applications and further theoretical research. Practically, the ability to render large-scale scenes in real-time opens up avenues for more dynamic and detailed virtual worlds in applications such as games, VR experiences, and the Metaverse. Theoretically, UE4-NeRF's LOD framework and scene partitioning could inspire further research into refined neural rendering techniques that efficiently manage large datasets and high-dimensional information.

Future research may explore optimizing the memory overhead associated with real-time rendering of even more extensive scenes. Additionally, extending the versatility of the system to support diverse hardware and reducing dependence on specific types of GPUs like NVIDIA's products could democratize the technology's deployment.

Conclusion

UE4-NeRF is a noteworthy advancement in the field of neural rendering, particularly for large-scale 3D scenes requiring real-time interaction. Its unique combination of NeRF-based scene representation, hierarchical mesh detailing, and tight integration with UE4 sets a new benchmark in rendering technologies. This pioneering approach not only highlights the versatility and adaptability of NeRF models but also drives the exploration of future potentials in dynamic and interactive computer-generated environments.

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GitHub

  1. GitHub - JamChaos/UE4-NeRF: The code for the UE4-NeRF. (137 stars)
  2. UE4-NeRF