“N-BVH: Neural Ray Queries With Bounding Volume Hierarchies”
Conference:
Type(s):
Title:
- N-BVH: Neural Ray Queries With Bounding Volume Hierarchies
Presenter(s)/Author(s):
Abstract:
N-BVH, a compressed neural architecture, enables efficient ray queries in rendering. Our method seamlessly integrates neural ray queries into standard pipelines. By optimizing parameters through an adaptive BVH-driven probing scheme, N-BVH can serve accurate ray queries from a compact representation while providing faithful approximations of visibility, depth, and appearance attributes.
References:
[1]
Pontus Andersson, Jim Nilsson, Tomas Akenine-M?ller, Magnus Oskarsson, Kalle ?str?m, and Mark D. Fairchild. 2020. FLIP: A Difference Evaluator for Alternating Images. ACM Comp. Graph. and Interactive Techn. 3, 2 (2020), 15:1?15:23.
[2]
Robert L. Cook, John Halstead, Maxwell Planck, and David Ryu. 2007. Stochastic simplification of aggregate detail. ACM Trans. Graph. 26, 3 (jul 2007), 79?es. https://doi.org/10.1145/1276377.1276476
[3]
Brandon Y. Feng, Yinda Zhang, Danhang Tang, Ruofei Du, and Amitabh Varshney. 2022. PRIF: Primary Ray-based Implicit Function. In Proceedings of the European Conference on Computer Vision (ECCV) (Tel Aviv, Israel). Springer-Verlag, Berlin, Heidelberg, 138?155. https://doi.org/10.1007/978-3-031-20062-5_9
[4]
Shin Fujieda, Chih Chen Kao, and Takahiro Harada. 2023. Neural Intersection Function. In High-Performance Graphics – Symposium Papers, Jacco Bikker and Christiaan Gribble (Eds.). The Eurographics Association. https://doi.org/10.2312/hpg.20231135
[5]
Michael Garland and Paul S. Heckbert. 1997. Surface Simplification Using Quadric Error Metrics. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques(SIGGRAPH ?97). ACM Press/Addison-Wesley Publishing Co., USA, 209?216. https://doi.org/10.1145/258734.258849
[6]
Enrico Gobbetti and Fabio Marton. 2005. Far voxels: a multiresolution framework for interactive rendering of huge complex 3D models on commodity graphics platforms. In ACM SIGGRAPH 2005 Papers (Los Angeles, California) (SIGGRAPH ?05). Association for Computing Machinery, New York, NY, USA, 878?885. https://doi.org/10.1145/1186822.1073277
[7]
Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, and Paul Debevec. 2021. Baking Neural Radiance Fields for Real-Time View Synthesis. ICCV (2021).
[8]
Hugues Hoppe. 1996. Progressive meshes. In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques(SIGGRAPH ?96). Association for Computing Machinery, New York, NY, USA, 99?108. https://doi.org/10.1145/237170.237216
[9]
Doyub Kim, Minjae Lee, and Ken Museth. 2022. NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks. arxiv:2208.04448 [cs.LG]
[10]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[11]
Leif Kobbelt, Swen Campagna, and Hans-Peter Seidel. 1998. A General Framework for Mesh Decimation. In Proc. Graph. Interface. 43?50.
[12]
Peter Lindstrom. 2000. Out-of-core simplification of large polygonal models. In ACM SIGGRAPH. 259?262.
[13]
Guillaume Loubet and Fabrice Neyret. 2017. Hybrid mesh-volume LoDs for all-scale pre-filtering of complex 3D assets. Computer Graphics Forum 36, 2 (2017), 431?442. https://doi.org/10.1111/cgf.13138 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13138
[14]
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. 2021. Acorn: Adaptive Coordinate Networks for Neural Scene Representation. ACM Trans. Graph. 40, 4, Article 58 (jul 2021), 13 pages. https://doi.org/10.1145/3450626.3459785
[15]
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In ECCV.
[16]
Thomas M?ller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. ACM Trans. Graph. 41, 4, Article 102 (July 2022). https://doi.org/10.1145/3528223.3530127
[17]
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18]
M. Pauly, M. Gross, and L.P. Kobbelt. 2002. Efficient simplification of point-sampled surfaces. In IEEE Visualization, 2002. VIS 2002.163?170. https://doi.org/10.1109/VISUAL.2002.1183771
[19]
J. Rossignac and P. Borrel. 1993. Multi-Resolution 3D Approximation for Rendering Complex Scenes. Modeling in Computer Graphics (1993), 455?465.
[20]
S. Schaefer and J. Warren. 2003. Adaptive Vertex Clustering Using Octrees. In Proceedings of SIAM Geometric Design and Computing. 491?500.
[21]
P.Y. Simard, D. Steinkraus, and J.C. Platt. 2003. Best practices for convolutional neural networks applied to visual document analysis. In Doc. Anal. and Recogn.958?963.
[22]
Martin Stich, Heiko Friedrich, and Andreas Dietrich. 2009. Spatial Splits in Bounding Volume Hierarchies. In Proceedings of the Conference on High Performance Graphics 2009 (New Orleans, Louisiana) (HPG ?09). Association for Computing Machinery, New York, NY, USA, 7?13. https://doi.org/10.1145/1572769.1572771
[23]
Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas M?ller, Morgan McGuire, Alec Jacobson, and Sanja Fidler. 2022. Variable Bitrate Neual fields.
[24]
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2021. Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. (2021).
[25]
Zian Wang, Tianchang Shen, Merlin Nimier-David, Nicholas Sharp, Jun Gao, Alexander Keller, Sanja Fidler, Thomas M?ller, and Zan Gojcic. 2023. Adaptive Shells for Efficient Neural Radiance Field Rendering. ACM Trans. Graph. 42, 6, Article 259 (2023), 15 pages. https://doi.org/10.1145/3618390
[26]
Philippe Weier, Tobias Zirr, Anton Kaplanyan, Ling-Qi Yan, and Philipp Slusallek. 2023. Neural Prefiltering for Correlation-Aware Levels of Detail. ACM Trans. Graph. 42, 4, Article 78 (jul 2023), 16 pages. https://doi.org/10.1145/3592443
[27]
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. 2022. Neural fields in visual computing and beyond. In Comp. Graph. Forum, Vol. 41. 641?676.
[28]
Runzhao Yang. 2023. TINC: Tree-structured Implicit Neural Compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
