A New Breakthrough in 3D Scene Reconstruction: In-Depth Guide to Distilled-3DGS

Introduction: Why Do We Need More Efficient 3D Scene Representation?

When we take panoramic photos with our smartphones, have you ever wondered how computers reconstruct 3D scenes that can be viewed from any angle? In recent years, 3D Gaussian Splatting (3DGS) technology has gained attention for its real-time rendering capabilities. However, just like how high-resolution photos consume significant storage space, traditional 3DGS models require storing millions of Gaussian distribution units, creating storage bottlenecks in practical applications.

This article will analyze the Distilled-3DGS technology proposed by a research team from Shanghai Jiao Tong University. This innovation uses knowledge distillation to significantly reduce storage requirements while maintaining rendering quality. We’ll explain its core principles in plain language and provide practical application recommendations.

1. Evolution of 3D Scene Reconstruction: From NeRF to 3DGS

1.1 Challenges of Traditional Methods

Early Neural Radiance Fields (NeRF) technology relies on complex neural networks for inference, similar to using a supercomputer for everyday document processing. While effective, efficiency became its biggest limitation.

Technology Evolution Timeline:

2020: NeRF introduced (High quality but slow)
2021: Plenoxels (Speed improvement but high storage)
2022: 3DGS (Real-time rendering but storage-intensive)
2023: Distilled-3DGS (Balancing quality and storage)

1.2 3DGS’s Technical Breakthrough

3DGS represents 3D scenes using millions of 3D Gaussian primitives, like composing a scene with countless glowing spheres. Each primitive has:

• Position (μ)
• Shape (covariance matrix Σ)
• Color (SH coefficients)
• Opacity (o)

While this explicit representation enables real-time rendering, it requires gigabytes of storage per scene, similar to how high-resolution panoramic photos need substantial storage.

2. Core Innovations of Distilled-3DGS

2.1 Knowledge Distillation: “Teachers” Guiding “Students”

Imagine training a new chef (student model) with guidance from multiple experienced chefs (teacher models):

1. Multi-Teacher Strategy: Simultaneously use standard, perturbed, and dropout models
2. Knowledge Transfer: Fuse multiple “teachers'” rendering results into pseudo-labels
3. Structure Preservation: Ensure the “student” learns similar spatial geometric distributions

2.2 Spatial Distribution Distillation: Preserving Geometric Features

Researchers proposed using voxel histograms to compare point cloud distributions, similar to comparing brushstroke patterns in two paintings:

1. Divide 3D space into 128×128×128 grids
2. Count points in each grid cell
3. Compare histogram distributions using cosine similarity

Unique advantages of this method:

• Unaffected by point cloud density
• Computationally efficient
• Low memory usage

3. Technical Implementation Details

3.1 Multi-Teacher Training Stage

Teacher Model Construction Methods:

# Standard teacher model
G_std = Base 3DGS model

# Perturbation-based teacher
Add random noise to Gaussian parameters during training:
G_perb = G_std + δ_t

# Random dropout teacher
Randomly deactivate Gaussian units:
Each Gaussian has 20% probability of being deactivated

3.2 Student Model Training

Loss Function Design:

Total Loss = Color reconstruction loss + Knowledge distillation loss + Structural similarity loss

Key Parameters:

• Training iterations: 30,000
• Voxel grid resolution: 128³
• Initial Gaussian count: 10-15% of 3DGS

4. Experimental Results Analysis

4.1 Performance Comparison (Mip-NeRF360 Dataset Example)

Data source: Table 1 in paper

4.2 Real-World Scene Performance

In complex “Garden” scene:

• 3DGS storage: 5.92GB
• Our method’s storage: 0.68GB
• PSNR improvement: 0.26dB

5. Technical Application Guide

5.1 Suitable Applications

• AR/VR Applications: Real-time rendering with limited storage
• Autonomous Driving: Efficient processing of large-scale 3D maps
• Digital Twins: Efficient storage of city-scale 3D scenes

5.2 Implementation Recommendations

Hardware Requirements:

• Training: NVIDIA RTX 3090 or higher
• Inference: Standard GPU sufficient

Deployment Steps:

1. Train teacher models using official 3DGS code
2. Apply perturbation and dropout strategies
3. Add structural similarity loss during student training
4. Iteratively reduce Gaussian count

6. Frequently Asked Questions

Q1: What advantages does this have over traditional model compression?

Traditional pruning relies on heuristic rules, while our method preserves more important scene structures, achieving 0.55dB PSNR improvement on Mip360 dataset.

Q2: Does it support dynamic scenes?

Current version primarily optimizes static scenes. Dynamic scenes require temporal dimension extensions.

Q3: What are the hardware performance requirements?

Inference requires only standard GPUs. Storage requirements reduced by over 80%, suitable for mobile deployment.

7. Future Directions

Researchers identified two main improvement directions:

1. Develop end-to-end distillation pipelines
2. Investigate adaptive parameter pruning strategies

These directions could further improve efficiency and promote practical applications of 3D scene reconstruction technology.

This article is based on the paper “Distilled-3DGS: Distilled 3D Gaussian Splatting” published by the Shanghai Jiao Tong University team at CVPR 2024. The full paper is available on arXiv (2508.14037v1).