title: RadSplat: Radiance Field-Informed Gaussian Splatting for Robust
    Real-Time Rendering with 900+ FPS
summary: Recent advances in view synthesis and real-time rendering have achieved
  photorealistic quality at impressive rendering speeds. While Radiance
  Field-based methods achieve state-of-the-art quality in challenging scenarios
  such as in-the-wild captures and large-scale scenes, they often suffer from
  excessively high compute requirements linked to volumetric rendering. Gaussian
  Splatting-based methods, on the other hand, rely on rasterization and naturally
  achieve real-time rendering but suffer from brittle optimization heuristics
  that underperform on more challenging scenes. In this work, we present
  RadSplat, a lightweight method for robust real-time rendering of complex
  scenes. Our main contributions are threefold. First, we use radiance fields as
  a prior and supervision signal for optimizing point-based scene
  representations, leading to improved quality and more robust optimization.
  Next, we develop a novel pruning technique reducing the overall point count
  while maintaining high quality, leading to smaller and more compact scene
  representations with faster inference speeds. Finally, we propose a novel
  test-time filtering approach that further accelerates rendering and allows to
  scale to larger, house-sized scenes. We find that our method enables
  state-of-the-art synthesis of complex captures at 900+ FPS.
paper_authors._id: 65fbb77da9d83c79a62bfc2e
paper_authors.name: Michael Niemeyer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T10:03:00.685Z
paper_authors._id: 63f4f16171a5d395c71d2d1a
paper_authors.avatarUrl: /avatars/0377c4e6b35f8bd1a29d38c99a5943c1.svg
paper_authors.fullname: Michael Niemeyer
paper_authors.type: user
paper_authors.user: mniemeyer
paper_authors._id: 65fbb77da9d83c79a62bfc2f
paper_authors.name: Fabian Manhardt
paper_authors._id: 65fbb77da9d83c79a62bfc30
paper_authors.name: Marie-Julie Rakotosaona
paper_authors._id: 65fbb77da9d83c79a62bfc31
paper_authors.name: Michael Oechsle
paper_authors._id: 65fbb77da9d83c79a62bfc32
paper_authors.name: Daniel Duckworth
paper_authors._id: 65fbb77da9d83c79a62bfc33
paper_authors.name: Rama Gosula
paper_authors._id: 65fbb77da9d83c79a62bfc34
paper_authors.name: Keisuke Tateno
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T10:03:49.309Z
paper_authors._id: 6304746734c824b1724b5b31
paper_authors.avatarUrl: /avatars/ec0518b7ec6ba36d0a21ea25c16fd93c.svg
paper_authors.fullname: Keisuke Tateno
paper_authors.type: user
paper_authors.user: tateno
paper_authors._id: 65fbb77da9d83c79a62bfc35
paper_authors.name: John Bates
paper_authors._id: 65fbb77da9d83c79a62bfc36
paper_authors.name: Dominik Kaeser
paper_authors._id: 65fbb77da9d83c79a62bfc37
paper_authors.name: Federico Tombari
paper_title: RadSplat: Radiance Field-Informed Gaussian Splatting for Robust
    Real-Time Rendering with 900+ FPS
paper_summary: Recent advances in view synthesis and real-time rendering have achieved
  photorealistic quality at impressive rendering speeds. While Radiance
  Field-based methods achieve state-of-the-art quality in challenging scenarios
  such as in-the-wild captures and large-scale scenes, they often suffer from
  excessively high compute requirements linked to volumetric rendering. Gaussian
  Splatting-based methods, on the other hand, rely on rasterization and naturally
  achieve real-time rendering but suffer from brittle optimization heuristics
  that underperform on more challenging scenes. In this work, we present
  RadSplat, a lightweight method for robust real-time rendering of complex
  scenes. Our main contributions are threefold. First, we use radiance fields as
  a prior and supervision signal for optimizing point-based scene
  representations, leading to improved quality and more robust optimization.
  Next, we develop a novel pruning technique reducing the overall point count
  while maintaining high quality, leading to smaller and more compact scene
  representations with faster inference speeds. Finally, we propose a novel
  test-time filtering approach that further accelerates rendering and allows to
  scale to larger, house-sized scenes. We find that our method enables
  state-of-the-art synthesis of complex captures at 900+ FPS.
paper_ai_keywords: view synthesis real-time rendering photorealistic quality Radiance Field-based methods volumetric rendering Gaussian Splatting-based methods rasterization RadSplat point-based scene representations radiance fields prior supervision signal pruning technique point count test-time filtering state-of-the-art synthesis FPS (Frames Per Second)