title: MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and
    Photorealism From Sparse Views
summary: We present a novel appearance model that simultaneously realizes explicit
  high-quality 3D surface mesh recovery and photorealistic novel view synthesis
  from sparse view samples. Our key idea is to model the underlying scene
  geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels
  (MAtCha Gaussians). MAtCha distills high-frequency scene surface details from
  an off-the-shelf monocular depth estimator and refines it through Gaussian
  surfel rendering. The Gaussian surfels are attached to the charts on the fly,
  satisfying photorealism of neural volumetric rendering and crisp geometry of a
  mesh model, i.e., two seemingly contradicting goals in a single model. At the
  core of MAtCha lies a novel neural deformation model and a structure loss that
  preserve the fine surface details distilled from learned monocular depths while
  addressing their fundamental scale ambiguities. Results of extensive
  experimental validation demonstrate MAtCha's state-of-the-art quality of
  surface reconstruction and photorealism on-par with top contenders but with
  dramatic reduction in the number of input views and computational time. We
  believe MAtCha will serve as a foundational tool for any visual application in
  vision, graphics, and robotics that require explicit geometry in addition to
  photorealism. Our project page is the following:
  https://anttwo.github.io/matcha/
paper_authors._id: 6758776c9dc2cbfe5f45ee7f
paper_authors.name: Antoine Guédon
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-10T18:24:51.244Z
paper_authors._id: 6489dd894bbed6144c870274
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6489dd894bbed6144c870274/c_J9b4DHwXeE5VtTeQ2ue.jpeg
paper_authors.fullname: Antoine Guédon
paper_authors.type: user
paper_authors.user: AntoineGuedon
paper_authors._id: 6758776c9dc2cbfe5f45ee80
paper_authors.name: Tomoki Ichikawa
paper_authors._id: 6758776c9dc2cbfe5f45ee81
paper_authors.name: Kohei Yamashita
paper_authors._id: 6758776c9dc2cbfe5f45ee82
paper_authors.name: Ko Nishino
paper_title: MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and
    Photorealism From Sparse Views
paper_summary: We present a novel appearance model that simultaneously realizes explicit
  high-quality 3D surface mesh recovery and photorealistic novel view synthesis
  from sparse view samples. Our key idea is to model the underlying scene
  geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels
  (MAtCha Gaussians). MAtCha distills high-frequency scene surface details from
  an off-the-shelf monocular depth estimator and refines it through Gaussian
  surfel rendering. The Gaussian surfels are attached to the charts on the fly,
  satisfying photorealism of neural volumetric rendering and crisp geometry of a
  mesh model, i.e., two seemingly contradicting goals in a single model. At the
  core of MAtCha lies a novel neural deformation model and a structure loss that
  preserve the fine surface details distilled from learned monocular depths while
  addressing their fundamental scale ambiguities. Results of extensive
  experimental validation demonstrate MAtCha's state-of-the-art quality of
  surface reconstruction and photorealism on-par with top contenders but with
  dramatic reduction in the number of input views and computational time. We
  believe MAtCha will serve as a foundational tool for any visual application in
  vision, graphics, and robotics that require explicit geometry in addition to
  photorealism. Our project page is the following:
  https://anttwo.github.io/matcha/
paper_ai_keywords: appearance model 3D surface mesh recovery photorealistic novel view synthesis sparse view samples Atlas of Charts MAtCha Gaussians Gaussian surfels monocular depth estimator neural volumetric rendering neural deformation model structure loss surface reconstruction computational time visual application vision graphics robotics explicit geometry