Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering

• URL: http://arxiv.org/abs/2412.04459v1
• Date: Thu, 05 Dec 2024 18:59:11 GMT
• Title: Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering
• Authors: Cheng Sun, Jaesung Choe, Charles Loop, Wei-Chiu Ma, Yu-Chiang Frank Wang,
• Abstract summary: We propose an efficient radiance field rendering algorithm that incorporates a radianceization process on sparse voxels without neural networks or 3D Gaussians. We adaptively fit sparse voxels to different levels of detail within scenes, faithfully reproducing details while achieving high rendering frame rates. Our method improves the previous neural-free voxel grid representation by over 4db PSNR and more than 10x rendering FPS speedup.
• Score: 37.48219196092378
• License:
• Abstract: We propose an efficient radiance field rendering algorithm that incorporates a rasterization process on sparse voxels without neural networks or 3D Gaussians. There are two key contributions coupled with the proposed system. The first is to render sparse voxels in the correct depth order along pixel rays by using dynamic Morton ordering. This avoids the well-known popping artifact found in Gaussian splatting. Second, we adaptively fit sparse voxels to different levels of detail within scenes, faithfully reproducing scene details while achieving high rendering frame rates. Our method improves the previous neural-free voxel grid representation by over 4db PSNR and more than 10x rendering FPS speedup, achieving state-of-the-art comparable novel-view synthesis results. Additionally, our neural-free sparse voxels are seamlessly compatible with grid-based 3D processing algorithms. We achieve promising mesh reconstruction accuracy by integrating TSDF-Fusion and Marching Cubes into our sparse grid system.

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