title: GS^3: Efficient Relighting with Triple Gaussian Splatting
summary: 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/.
paper_authors._id: 670f1d32cdc5f5a98019d700
paper_authors.name: Zoubin Bi
paper_authors._id: 670f1d32cdc5f5a98019d701
paper_authors.name: Yixin Zeng
paper_authors._id: 670f1d32cdc5f5a98019d702
paper_authors.name: Chong Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-16T09:43:50.463Z
paper_authors._id: 60747cbf3ea03830676542b5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60747cbf3ea03830676542b5/wGr1Jzz520JM9nZ-UcLyb.png
paper_authors.fullname: Chong Zeng
paper_authors.type: user
paper_authors.user: NCJ
paper_authors._id: 670f1d32cdc5f5a98019d703
paper_authors.name: Fan Pei
paper_authors._id: 670f1d32cdc5f5a98019d704
paper_authors.name: Xiang Feng
paper_authors._id: 670f1d32cdc5f5a98019d705
paper_authors.name: Kun Zhou
paper_authors._id: 670f1d32cdc5f5a98019d706
paper_authors.name: Hongzhi Wu
paper_title: GS^3: Efficient Relighting with Triple Gaussian Splatting
paper_summary: 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/.
paper_ai_summary: 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.
paper_ai_keywords: 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