DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model
This addresses a critical problem in autonomous driving for improved perception, but it is incremental as it builds on existing diffusion models and depth completion techniques.
The paper tackles depth completion for autonomous driving by proposing DenseFormer, a method that uses a conditional diffusion model to generate dense depth maps from sparse depth and RGB images, achieving state-of-the-art performance on the KITTI dataset.
The depth completion task is a critical problem in autonomous driving, involving the generation of dense depth maps from sparse depth maps and RGB images. Most existing methods employ a spatial propagation network to iteratively refine the depth map after obtaining an initial dense depth. In this paper, we propose DenseFormer, a novel method that integrates the diffusion model into the depth completion task. By incorporating the denoising mechanism of the diffusion model, DenseFormer generates the dense depth map by progressively refining an initial random depth distribution through multiple iterations. We propose a feature extraction module that leverages a feature pyramid structure, along with multi-layer deformable attention, to effectively extract and integrate features from sparse depth maps and RGB images, which serve as the guiding condition for the diffusion process. Additionally, this paper presents a depth refinement module that applies multi-step iterative refinement across various ranges to the dense depth results generated by the diffusion process. The module utilizes image features enriched with multi-scale information and sparse depth input to further enhance the accuracy of the predicted depth map. Extensive experiments on the KITTI outdoor scene dataset demonstrate that DenseFormer outperforms classical depth completion methods.