GSCore: Efficient Radiance Field Rendering via Architectural Support for 3D Gaussian Splatting
Junseo Lee, Seokwon Lee, Jungi Lee, Junyong Park, Jaewoong Sim
摘要
This paper presents GSCore, a hardware acceleration unit that efficiently executes the rendering pipeline of 3D Gaussian Splatting with algorithmic optimizations. GSCore builds on the observations from an in-depth analysis of Gaussian-based radiance field rendering to enhance computational efficiency and bring the technique to wide adoption. In doing so, we present several optimization techniques, Gaussian shape-aware intersection test, hierarchical sorting, and subtile skipping, all of which are synergistically integrated with GSCore. We implement the hardware design of GSCore, synthesize it using a commercial 28nm technology, and evaluate the performance across a range of synthetic and real-world scenes with varying image resolutions. Our evaluation results show that GSCore achieves a 15.86× speedup on average over the mobile consumer GPU with a substantially smaller area and lower energy consumption.
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引用它的顶会 Paper25
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它引用的顶会 Paper16
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan 等CVPR 2022 · 被引用 1,603 次
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua 等NeurIPS 2020 · 被引用 1,535 次
- Plenoxels: Radiance Fields without Neural NetworksSara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen 等CVPR 2022 · 被引用 1,237 次
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