3D Gaussian Splatting for Real-Time Radiance Field Rendering

Introduction

3D Gaussian Splatting is a transformative approach in the realm of computer graphics, offering an innovative technique for real-time rendering of photorealistic scenes. This method has gained significant attention due to its ability to handle scenes captured from multiple photos or videos, balancing the need for high visual quality with the demands of real-time performance, especially for complete scenes rendered at 1080p resolution​​​​.

Core Concept of 3D Gaussian Splatting

At its heart, 3D Gaussian Splatting is a rasterization technique. In traditional computer graphics, rasterization often involves drawing triangles on the screen. However, in Gaussian Splatting, the fundamental unit is the Gaussian. Each Gaussian is defined by its position (XYZ coordinates), covariance (a 3x3 matrix determining how it’s stretched or scaled), color (RGB values), and alpha (transparency level)​​.

Workflow and Implementation

1. Structure from Motion (SfM)

The process begins with the Structure from Motion (SfM) method. SfM is used to estimate a 3D point cloud from a set of 2D images. This point cloud forms the basis of the scene representation. Libraries like COLMAP can be utilized for this purpose​​.

# Pseudocode for generating a point cloud using Structure from Motion (SfM)
# Import a library like COLMAP for SfM
import colmap

# Load your set of 2D images
images = load_images("path_to_images")

# Estimate the 3D point cloud from 2D images
point_cloud = colmap.estimate_point_cloud(images)

Conversion to Gaussians

Each point in the SfM-generated cloud is converted into a Gaussian. This step is critical for rasterization, but to achieve high-quality results, further training of this representation is required​​.

# Pseudocode for converting points to Gaussians
for point in point_cloud:
    gaussian = create_gaussian(point)
    scene.add(gaussian)

Training Procedure

The training involves a series of steps:

• Rasterization: The…