Abstract
The advent of neural 3D Gaussians [21] has recently brought about a revolution in the field of neural rendering, facilitating the generation of high-quality renderings at real-time speeds. However, the explicit and discrete repre-sentation encounters challenges when applied to scenes fea-turing reflective surfaces. In this paper, we present Gaus-sian Shader, a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural ren-dering in scenes with reflective surfaces while preserving the training and rendering efficiency. The main challenge in applying the shading function lies in the accurate nor-mal estimation on discrete 3D Gaussians. Specifically, we proposed a novel normal estimation framework based on the shortest axis directions of 3D Gaussians with a deli-cately designed loss to make the consistency between the normals and the geometries of Gaussian spheres. Experiments show that GaussianShader strikes a commendable balance between efficiency and visual quality. Our method surpasses Gaussian Splatting [21] in PSNR on specular object datasets, exhibiting an improvement of 1.57dB. When compared to prior works handling reflective surfaces, such as Ref-NeRF [45], our optimization time is significantly accelerated (23h vs. 0.58h).
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 |
| Publisher | IEEE Computer Society |
| Pages | 5322-5332 |
| Number of pages | 11 |
| ISBN (Electronic) | 9798350353006 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States Duration: 16 Jun 2024 → 22 Jun 2024 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 |
|---|---|
| Country/Territory | United States |
| City | Seattle |
| Period | 16/06/24 → 22/06/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
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