Underwater 3D images reconstruction via guided diffusion for streak tube imaging LiDAR

Accurate and robust 3D reconstruction for underwater streak tube imaging LiDAR (STIL) is hindered by weak echo signals and poor imaging quality due to high scattering, absorption, and turbidity. Traditional signal processing-based enhancement methods often neglect the semantic and distributional information of reconstructed objects. To address this limitation, we propose an RGBD diffusion model for denoising reconstructed images via generative refinement. Given the scarcity of underwater training data, we pre-train the diffusion model on an external dataset. To further enforce consistency between the generated content and the input, we incorporate a state-of-the-art cross-correlation algorithm (CCA) to guide the low-frequency components during the diffusion process. Experimental results demonstrate that our approach achieves higher accuracy and lower errors compared to existing methods, with a depth resolution surpassing 0.55 mm under a 0.14 m water attenuation length, significantly enhancing underwater STIL imaging performance.