3D Gaussian Splatting vs. NeRFs. What is the difference? 🤔

In the world of computer vision, 3D Gaussian Splatting and NeRFs are gaining traction.

Curiosity: But what sets them apart? Here’s a quick breakdown:

Comparison Overview

Retrieve: Key differences between NeRF and 3D Gaussian Splatting.

graph TB
    A[3D Scene Representation] --> B[NeRF]
    A --> C[3D Gaussian Splatting]
    
    B --> B1[Continuous Space]
    B --> B2[Neural Network]
    B --> B3[Point Sampling]
    
    C --> C1[Sparse Points]
    C --> C2[Direct Optimization]
    C --> C3[Gaussian Ellipsoids]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style C fill:#d4edda

Detailed Comparison

Aspect	NeRF	3D Gaussian Splatting
3D Space	Continuous	Sparse points
Point Generation	Sampling per image	Structure from Motion
Representation	RGBA + viewing direction	Gaussians (shape, size, transparency, color)
Color Description	View-dependent	Spherical harmonics
Optimization	Neural network	Direct optimization
Output	Continuous function	Discrete ellipsoids
Approach	Neural, continuous	Geometric, discrete

1. 3D Space Representation

Retrieve: Different approaches to representing 3D space.

NeRF:

• Creates continuous 3D space
• Samples points throughout scene
• Per training image sampling

Gaussian Splatting:

• Relies on sparse 3D points
• Often uses Structure from Motion
• More efficient representation

2. Point Description

Retrieve: How each method describes scene points.

NeRF:

• RGBA color per point
• Viewing direction dependency
• Appearance varies with location and angle

Gaussian Splatting:

• Complex 3D Gaussian forms
• Varying shapes, sizes, transparency
• Color described with spherical harmonics
• More flexible representation

3. Optimization

Innovate: Different optimization strategies.

NeRF:

• Neural network learns continuous function
• Color and opacity functions
• Implicit representation

Gaussian Splatting:

• Direct optimization of ellipsoid properties
• No neural network needed
• Explicit discrete structure
• Faster training

Architecture Comparison

graph LR
    A[Input Images] --> B[NeRF]
    A --> C[Gaussian Splatting]
    
    B --> D[Neural Network]
    D --> E[Continuous Function]
    
    C --> F[Direct Optimization]
    F --> G[Discrete Gaussians]
    
    style B fill:#fff3cd
    style C fill:#d4edda

When to Use Each

Retrieve: Choosing the right approach for your application.

Use Case	Recommended	Reason
High Quality	NeRF	Continuous representation
Fast Training	Gaussian Splatting	Direct optimization
Real-time Rendering	Gaussian Splatting	Efficient discrete structure
Research	NeRF	Neural approach flexibility
Production	Gaussian Splatting	Faster, more practical

Key Takeaways

Retrieve: NeRF offers a continuous, neural approach to 3D scene representation, while 3D Gaussian Splatting provides a simpler, directly optimized discrete structure with faster training and rendering.

Innovate: By understanding the trade-offs between continuous neural representations and discrete geometric approaches, you can choose the right method for your specific application—whether prioritizing quality, speed, or practicality.

Curiosity → Retrieve → Innovation: Start with curiosity about 3D scene representation, retrieve insights from comparing NeRF and Gaussian Splatting, and innovate by applying the right approach to your 3D graphics or AR applications.

Next Steps:

• Explore NeRF implementations
• Try 3D Gaussian Splatting
• Compare performance
• Choose based on your needs

Information About 3D Gaussian Splatting

• Blog : https://xoft.tistory.com/74

Translate to Korean

컴퓨터 비전의 세계에서는 3D Gaussian Splatting 및 NeRF가 주목을 받고 있습니다. 그러나 무엇이 그들을 차별화합니까? 다음은 간단한 분석입니다.

🔍 3D 공간 표현:

• NeRF: 각 학습 이미지에 대해 장면 전체의 지점을 샘플링하여 연속 3D 공간을 만듭니다.
• 가우시안 스플래팅(Gaussian Splatting): 종종 모션의 구조(Structure from Motion)를 사용하여 생성되는 희소 3D 포인트 세트를 사용합니다.

🎨 포인트 설명:

• 가우시안 스플래팅: 구형 고조파로 설명되는 다양한 모양, 크기, 투명도 및 색상을 가진 가우시안이라는 복잡한 3D 형태를 사용합니다.
• NeRF: 각 포인트에 RGBA 색상과 보기 방향을 할당하며, 모양은 위치 및 시야각에 따라 달라집니다.

⚙️ 최적화:

• NeRF: 신경망을 사용하여 색상 및 불투명도에 대한 연속 함수를 학습합니다.
• Gaussian Splatting: 신경망 없이 각 3D 타원체의 속성을 직접 최적화하여 개별 타원체 세트를 생성합니다.

본질적으로 NeRF는 연속적인 신경 접근 방식을 제공하는 반면, Gaussian Splatting은 더 간단하고 직접 최적화된 이산 구조를 제공합니다.

3D 그래픽과 AR 애플리케이션에 어떤 접근 방식이 더 흥미롭다고 생각하십니까? 댓글로 생각을 공유하세요! 💬