ADC-GS: Pose-Free 3D Gaussian Splatting with Adaptive Depth Consistency

Abstract

Recently proposed 3D Gaussian Splatting (3DGS) has achieved state-of-the-art results in the fields of novel view synthesis, but it heavily relies on pre-computed camera poses. Although recent methods mitigate by leveraging explicit representations achieve novel view synthesis without requiring camera poses, Gaussians lack an appropriate growth strategy and accurate geometric information, which may lead to noticeable artifacts and distortions, particularly in complex scenes with large camera movements. To address these issues, we propose ADC-GS, where we introduce an Adaptive Depth Alignment (ADA) strategy to utilize monocular depth information from adjacent frames as guidance for camera pose optimization and provides geometric supervision through depth consistency for training global Gaussians. Furthermore, we demonstrate the drawbacks of the progressive growth strategy in Gaussians and further enhance reconstruction quality by refining the growth strategy. Extensive experiments on the challenging Tanks and Temples dataset show that our method achieves state-of-the-art results in both novel view synthesis quality and pose estimation accuracy.