title: Splatter Image: Ultra-Fast Single-View 3D Reconstruction
summary: We introduce the Splatter Image, an ultra-fast approach for monocular 3D
  object reconstruction which operates at 38 FPS. Splatter Image is based on
  Gaussian Splatting, which has recently brought real-time rendering, fast
  training, and excellent scaling to multi-view reconstruction. For the first
  time, we apply Gaussian Splatting in a monocular reconstruction setting. Our
  approach is learning-based, and, at test time, reconstruction only requires the
  feed-forward evaluation of a neural network. The main innovation of Splatter
  Image is the surprisingly straightforward design: it uses a 2D image-to-image
  network to map the input image to one 3D Gaussian per pixel. The resulting
  Gaussians thus have the form of an image, the Splatter Image. We further extend
  the method to incorporate more than one image as input, which we do by adding
  cross-view attention. Owning to the speed of the renderer (588 FPS), we can use
  a single GPU for training while generating entire images at each iteration in
  order to optimize perceptual metrics like LPIPS. On standard benchmarks, we
  demonstrate not only fast reconstruction but also better results than recent
  and much more expensive baselines in terms of PSNR, LPIPS, and other metrics.
paper_authors._id: 6583be11948899c4539e809b
paper_authors.name: Stanislaw Szymanowicz
paper_authors._id: 6583be11948899c4539e809c
paper_authors.name: Christian Rupprecht
paper_authors._id: 6583be11948899c4539e809d
paper_authors.name: Andrea Vedaldi
paper_title: Splatter Image: Ultra-Fast Single-View 3D Reconstruction
paper_summary: We introduce the Splatter Image, an ultra-fast approach for monocular 3D
  object reconstruction which operates at 38 FPS. Splatter Image is based on
  Gaussian Splatting, which has recently brought real-time rendering, fast
  training, and excellent scaling to multi-view reconstruction. For the first
  time, we apply Gaussian Splatting in a monocular reconstruction setting. Our
  approach is learning-based, and, at test time, reconstruction only requires the
  feed-forward evaluation of a neural network. The main innovation of Splatter
  Image is the surprisingly straightforward design: it uses a 2D image-to-image
  network to map the input image to one 3D Gaussian per pixel. The resulting
  Gaussians thus have the form of an image, the Splatter Image. We further extend
  the method to incorporate more than one image as input, which we do by adding
  cross-view attention. Owning to the speed of the renderer (588 FPS), we can use
  a single GPU for training while generating entire images at each iteration in
  order to optimize perceptual metrics like LPIPS. On standard benchmarks, we
  demonstrate not only fast reconstruction but also better results than recent
  and much more expensive baselines in terms of PSNR, LPIPS, and other metrics.
paper_ai_keywords: Gaussian Splatting Splatter Image monocular 3D object reconstruction real-time rendering feed-forward evaluation neural network 2D image-to-image network 3D Gaussian cross-view attention renderer LPIPS PSNR perceptual metrics