3D Gaussian Splatting for Real-Time Radiance Field Rendering

TL;DR

This paper introduces a novel 3D Gaussian splatting method that enables real-time, high-quality radiance field rendering at 1080p resolution, combining scene representation, optimization, and fast rendering techniques.

Contribution

The paper presents a new 3D Gaussian-based scene representation and a visibility-aware rendering algorithm that together achieve real-time, high-quality novel-view synthesis.

Findings

Achieves real-time rendering at 1080p resolution with high visual quality.

Outperforms existing methods in visual quality and speed on standard datasets.

Supports anisotropic splatting for improved scene representation.

Abstract

Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.