RadSplat: Radiance Field-Informed Gaussian Splatting for Robust
  Real-Time Rendering with 900+ FPS
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.
65fbb77da9d83c79a62bfc2e
Michael Niemeyer
admin_assigned
2024-03-21T10:03:00.685Z
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/avatars/0377c4e6b35f8bd1a29d38c99a5943c1.svg
Michael Niemeyer
user
mniemeyer
65fbb77da9d83c79a62bfc2f
Fabian Manhardt
65fbb77da9d83c79a62bfc30
Marie-Julie Rakotosaona
65fbb77da9d83c79a62bfc31
Michael Oechsle
65fbb77da9d83c79a62bfc32
Daniel Duckworth
65fbb77da9d83c79a62bfc33
Rama Gosula
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Keisuke Tateno
admin_assigned
2024-03-21T10:03:49.309Z
6304746734c824b1724b5b31
/avatars/ec0518b7ec6ba36d0a21ea25c16fd93c.svg
Keisuke Tateno
user
tateno
65fbb77da9d83c79a62bfc35
John Bates
65fbb77da9d83c79a62bfc36
Dominik Kaeser
65fbb77da9d83c79a62bfc37
Federico Tombari
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust
  Real-Time Rendering with 900+ FPS
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.
RadSplat combines radiance fields and Gaussian splatting for efficient real-time rendering of complex scenes, achieving state-of-the-art performance.
view synthesis real-time rendering photorealistic quality Radiance Field-based methods Gaussian Splatting-based methods radiance fields point-based scene representations pruning technique test-time filtering approach