OC-CDGS: Octree-structured Gaussian splatting with combined depth regularization for sparse view synthesis

Abstract

High-fidelity novel view synthesis from sparse inputs remains a significant challenge in 3D reconstruction. Traditional methods relying on Multi-layer Perceptrons (MLPs) often struggle with overfitting and inaccurate geometric representations, especially with limited training views. To address these issues, we introduce OC-CDGS, a novel framework leveraging Octree-structured 3D Gaussian Splatting (3DGS) with Combined Depth Regularization (CDR). OC-CDGS organizes sparse point clouds into hierarchical octree-structured anchors, enabling efficient decoding of neural Gaussian attributes through compact MLPs. The CDR approach integrates global structure, local details, and gradient consistency to enhance geometric supervision. Additionally, we incorporate a Neural Gaussian Random Dropout Strategy (NGDS) to improve representation robustness and a Scene-Extent- Based Anchor Densification Strategy (SADS) to enhance anchor coverage in peripheral regions. Extensive experiments on MipNeRF360 and LLFF datasets demonstrate that OC-CDGS achieves state-of-the-art performance with real-time rendering capabilities, preserving high-quality geometric details.