arxiv_cv 95% Match Research Paper Computer graphics researchers,3D artists,VR/AR developers,AI researchers in 3D vision 4 days ago

DC4GS: Directional Consistency-Driven Adaptive Density Control for 3D Gaussian Splatting

computer-vision › 3d-vision

📄 Abstract

Abstract: We present a Directional Consistency (DC)-driven Adaptive Density Control (ADC) for 3D Gaussian Splatting (DC4GS). Whereas the conventional ADC bases its primitive splitting on the magnitudes of positional gradients, we further incorporate the DC of the gradients into ADC, and realize it through the angular coherence of the gradients. Our DC better captures local structural complexities in ADC, avoiding redundant splitting. When splitting is required, we again utilize the DC to define optimal split positions so that sub-primitives best align with the local structures than the conventional random placement. As a consequence, our DC4GS greatly reduces the number of primitives (up to 30% in our experiments) than the existing ADC, and also enhances reconstruction fidelity greatly.

Authors (4)

Moonsoo Jeong

Dongbeen Kim

Minseong Kim

Sungkil Lee

Submitted

October 30, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

DC4GS introduces a Directional Consistency (DC)-driven Adaptive Density Control (ADC) for 3D Gaussian Splatting. By incorporating the direction of positional gradients (DC) into ADC, it better captures local structural complexities, leading to more efficient primitive splitting and improved reconstruction fidelity with fewer primitives.

Business Value

Enables faster and more efficient creation of high-quality 3D assets and scenes for applications like VR/AR content creation, virtual production, and digital twins.

Paper Metadata

Innovation Type

Algorithmic Enhancement for 3D Reconstruction

Deployment Feasibility

High, as it enhances an existing popular technique (Gaussian Splatting) and focuses on efficiency and quality improvements.

Limitations Addressed

Redundant primitive splitting and suboptimal placement in conventional ADC for 3D Gaussian Splatting, which can hinder reconstruction fidelity and efficiency.

Performance Gains

Greatly reduces the number of primitives (up to 30%) and enhances reconstruction fidelity compared to existing ADC methods.

Technical Tags

3D Gaussian Splattingadaptive density controldirectional consistency3D reconstructionrenderingneural renderingcomputer graphicsscene representationgradient analysisstructural complexity

Research Topics

3D ReconstructionNeural RenderingComputer GraphicsScene RepresentationGaussian Splatting

Methods & Architectures

Directional Consistency (DC)-driven Adaptive Density Control (ADC)Incorporating gradient direction into ADCAngular coherence of gradientsOptimized primitive splitting 3D Gaussian Splatting

Applications & Tasks

Computer Graphics Virtual Reality Augmented Reality 3D Modeling Robotics (scene understanding) Efficient and high-fidelity 3D reconstructionOptimizing density control in Gaussian Splatting Improving the quality and efficiency of 3D reconstruction using Gaussian Splatting

Datasets & Benchmarks

Benchmarks

Reduces number of primitives by up to 30%

Reconstruction fidelityNumber of primitivesRendering quality

Related Fields

Computer GraphicsComputer Vision3D ModelingVirtual RealityAugmented Reality

Keywords

3D Gaussian Splattingneural rendering3D reconstructionadaptive density controldirectional consistencycomputer graphicsscene representationefficient rendering3D modelingDC4GS

Academic Context

#3D Reconstruction#Neural Rendering#Computer Graphics#Scene Representation#Gaussian Splatting

Technology Stack

Frameworks & Libraries

PyTorchCUDA

Programming Languages

C++Python

Commercial Potential

Potential Products

3D asset creation toolsReal-time rendering enginesVR/AR scene generation platforms

Target Industries

GamingEntertainmentArchitectureEngineeringE-commerce (3D product visualization)

Use Case Examples

Creating photorealistic 3D environments for games or filmsGenerating detailed 3D models from captured dataEnabling real-time rendering of complex scenes in VR/AR

Competitive Edge

DC4GS improves upon existing adaptive density control methods for 3D Gaussian Splatting by leveraging directional consistency, offering better efficiency and fidelity.

Market Opportunity

Growing markets for VR/AR, metaverse development, and real-time 3D content creation.

Revenue Models

Licensing of rendering technologyintegration into software suitescloud-based 3D rendering services.

Resource Requirements

Compute Needs

Moderate to High (GPU acceleration is essential for efficient rendering and training)

Data Requirements

3D scene data (e.g., from photogrammetry, NeRF datasets)

Deployment Constraints

Real-time rendering performance, memory requirements for large scenes, hardware compatibility.

Scalability

Scalability is good, as it aims to reduce the number of primitives, making larger scenes more manageable.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years for integration into graphics engines and 3D tools

Patent Potential

Moderate (novel algorithmic approach to density control)

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