title: 3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
summary: We present 3DGS-LM, a new method that accelerates the reconstruction of 3D
  Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored
  Levenberg-Marquardt (LM). Existing methods reduce the optimization time by
  decreasing the number of Gaussians or by improving the implementation of the
  differentiable rasterizer. However, they still rely on the ADAM optimizer to
  fit Gaussian parameters of a scene in thousands of iterations, which can take
  up to an hour. To this end, we change the optimizer to LM that runs in
  conjunction with the 3DGS differentiable rasterizer. For efficient GPU
  parallization, we propose a caching data structure for intermediate gradients
  that allows us to efficiently calculate Jacobian-vector products in custom CUDA
  kernels. In every LM iteration, we calculate update directions from multiple
  image subsets using these kernels and combine them in a weighted mean. Overall,
  our method is 30% faster than the original 3DGS while obtaining the same
  reconstruction quality. Our optimization is also agnostic to other methods that
  acclerate 3DGS, thus enabling even faster speedups compared to vanilla 3DGS.
paper_authors._id: 66ecdd0ae2976258bf89c60c
paper_authors.name: Lukas Höllein
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-20T12:43:32.384Z
paper_authors._id: 638711fd20244d72a73eb27b
paper_authors.avatarUrl: /avatars/162a798948bfe3df8e788aba5d024ffb.svg
paper_authors.fullname: Lukas Hoellein
paper_authors.type: user
paper_authors.user: lukasHoel
paper_authors._id: 66ecdd0ae2976258bf89c60d
paper_authors.name: Aljaž Božič
paper_authors._id: 66ecdd0ae2976258bf89c60e
paper_authors.name: Michael Zollhöfer
paper_authors._id: 66ecdd0ae2976258bf89c60f
paper_authors.name: Matthias Nießner
paper_title: 3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
paper_summary: We present 3DGS-LM, a new method that accelerates the reconstruction of 3D
  Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored
  Levenberg-Marquardt (LM). Existing methods reduce the optimization time by
  decreasing the number of Gaussians or by improving the implementation of the
  differentiable rasterizer. However, they still rely on the ADAM optimizer to
  fit Gaussian parameters of a scene in thousands of iterations, which can take
  up to an hour. To this end, we change the optimizer to LM that runs in
  conjunction with the 3DGS differentiable rasterizer. For efficient GPU
  parallization, we propose a caching data structure for intermediate gradients
  that allows us to efficiently calculate Jacobian-vector products in custom CUDA
  kernels. In every LM iteration, we calculate update directions from multiple
  image subsets using these kernels and combine them in a weighted mean. Overall,
  our method is 30% faster than the original 3DGS while obtaining the same
  reconstruction quality. Our optimization is also agnostic to other methods that
  acclerate 3DGS, thus enabling even faster speedups compared to vanilla 3DGS.
paper_ai_summary: 3DGS-LM improves 3D Gaussian Splatting speed by replacing the ADAM optimizer with a tailored Levenberg-Marquardt algorithm and optimizing GPU parallelization.
paper_ai_keywords: ADAM optimizer Levenberg-Marquardt (LM) 3D Gaussian Splatting Gaussians differentiable rasterizer Jacobian-vector products CUDA kernels