GS^3: Efficient Relighting with Triple Gaussian Splatting
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/.
670f1d32cdc5f5a98019d700
Zoubin Bi
670f1d32cdc5f5a98019d701
Yixin Zeng
670f1d32cdc5f5a98019d702
Chong Zeng
claimed_verified
2024-10-16T09:43:50.463Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/60747cbf3ea03830676542b5/wGr1Jzz520JM9nZ-UcLyb.png
Chong Zeng
user
NCJ
670f1d32cdc5f5a98019d703
Fan Pei
670f1d32cdc5f5a98019d704
Xiang Feng
670f1d32cdc5f5a98019d705
Kun Zhou
670f1d32cdc5f5a98019d706
Hongzhi Wu
GS^3: Efficient Relighting with Triple Gaussian Splatting
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/.
A novel lighting-and-view synthesis method using spatial and angular Gaussian representations and triple splatting achieves high-quality results in real-time with efficient training and rendering.
Gaussian representation triple splatting novel lighting-and-view synthesis multi-view point-lit input Lambertian angular Gaussians reflectance function multi-layer perceptron neural network global illumination rendering speed quality/performance