Adjacent-view Transformers for Supervised Surround-view Depth Estimation

The recent paper titled 'Adjacent-view Transformers for Supervised Surround-view Depth Estimation' introduces a novel approach to depth estimation, a critical component for 3D perception in robotics and autonomous driving. Traditional methods have primarily relied on front-view cameras, particularly within the KITTI benchmark, which limits their effectiveness. The AVT-SSDepth method proposed in this paper leverages a global-to-local feature extraction module that integrates CNN and transformer layers, allowing for richer representations. Furthermore, it introduces an adjacent-view attention mechanism that facilitates both intra-view and inter-view feature propagation, enhancing the depth estimation process across multiple cameras. Extensive experiments validate its superior performance over existing state-of-the-art methods on datasets like DDAD and nuScenes, showcasing its strong cross-dataset generalization. This advancement not only addresses previous research limitations but also h…