DC4GS: Directional Consistency-Driven Adaptive Density Control for 3D Gaussian Splatting
This work addresses efficiency and quality in 3D reconstruction for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled the problem of reducing primitive count in 3D Gaussian Splatting by introducing Directional Consistency into Adaptive Density Control, resulting in up to 30% fewer primitives and improved reconstruction fidelity.
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.