GS^3: Efficient Relighting with Triple Gaussian Splatting

• URL: http://arxiv.org/abs/2410.11419v1
• Date: Tue, 15 Oct 2024 09:11:30 GMT
• Title: GS^3: Efficient Relighting with Triple Gaussian Splatting
• Authors: Zoubin Bi, Yixin Zeng, Chong Zeng, Fan Pei, Xiang Feng, Kun Zhou, Hongzhi Wu,
• Abstract summary: We present a real-time, high-quality novel lighting-and-view from point-lit input images. We employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU.
• Score: 20.950152448082623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance. Our code and data are publicly available at https://GSrelight.github.io/.

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