Learning Monocular Depth via Cascaded Iterative Refinement in Visual-Echo Scenes

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Abstract
In recent years, integrating multimodal information, particularly visual and echo data, has shown great promise for improving depth estimation performance. While existing works demonstrate that combining binaural echo features with image attributes can enhance depth estimation, they often use rudimentary feature alignment and fusion methods, failing to fully exploit the complementary nature of cross-modal information and limiting integration effectiveness. To address these challenges, this paper introduces an innovative multimodal fusion framework. First, the framework incorporates a combination of multi-scale self-attention and cross-attention mechanisms, establishing correlations between features and facilitating cohesive interactions between the visual and echo domains. Furthermore, we propose an incremental feature updating mechanism based on Convolutional Gated Recurrent Units (ConvGRU), which implements cascaded iterative optimization, integrating contextual features with the multi-scale fused features from both echo and image modalities. In each iteration, the framework preserves contextual information from previous steps while employing a multi-level loss function to guide result updates. This approach effectively captures spatial structural information and progressively enhances depth estimation accuracy. Comprehensive experimental evaluations on the Replica, Matterport3D and BatVision (BV1) datasets validate the effectiveness of the proposed method. Comparative analyses with state-of-the-art monocular plus echo methods underscore the superior performance achievable through this novel framework.
Funding Information
  • National Natural Science Foundation of China (U2033218)