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

2025-10-30

AI Summary

In 3D Gaussian splatting, conventional adaptive density control (ADC) relies solely on gradient magnitude, leading to redundant splitting, inaccurate spatial localization, and insufficient representation of local geometric structures. To address these issues, this paper proposes a novel ADC method grounded in gradient direction consistency. By explicitly modeling angular coherence of gradients, our approach jointly leverages both gradient magnitude and direction to determine optimal splitting timing and spatial placement—thereby eliminating stochastic partitioning and enhancing structural alignment and density efficiency. Experimental results demonstrate that, while preserving reconstruction fidelity, the method significantly reduces primitive redundancy: up to 30% fewer Gaussians are required on standard benchmarks. Concurrently, it improves PSNR and visual quality. This work establishes a new paradigm for efficient, high-fidelity neural rendering.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Reduces primitive count in 3D Gaussian Splatting
• Improves reconstruction fidelity through directional consistency
• Optimizes primitive splitting using gradient angular coherence

Innovation

• Uses directional consistency for adaptive density control
• Employs angular coherence to capture structural complexities
• Optimizes split positions to align with local structures