RadSplat: Radiance Field-Informed Gaussian Splatting for Robust
  Real-Time Rendering with 900+ FPS
Authors: name: Michael Niemeyer, name: Fabian Manhardt, name: Marie-Julie Rakotosaona, name: Michael Oechsle, name: Daniel Duckworth, name: Rama Gosula, name: Keisuke Tateno, name: John Bates, name: Dominik Kaeser, name: Federico Tombari

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.