Enhancing Multi-View Omnidirectional Depth Estimation With Semantic-Aware Cost Aggregation and Spatial Propagation

Omnidirectional depth estimation predicts 360-degree depth information using multiple fisheye cameras arranged in a surround-view configuration. However, due to the lack of reference panorama and differences between the predicted depth viewpoint and input cameras, it is challenging to construct and utilize semantic information to improve depth accuracy, resulting in limited accurate in complex regions such as non-overlapping, weak textures, object boundaries and occlusions. This paper proposes a novel model architecture that effectively extracts and leverages semantic information to enhance the accuracy of omnidirectional depth estimation. Specifically, the proposed algorithm combines the variance and mean of multi-view image features to construct the fused matching cost and utilize both geometry and semantic constraints. The model extracts 360-degree semantic context during matching cost aggregation, and predict the corresponding panoramas jointly with omnidirectional depth maps. A semantic-aware spatial propagation module is then employed to further refine the depth estimation. We leverage a multi-scale multi-task learning strategy to supervise the prediction of omnidirectional depth maps and panoramas jointly. The proposed approach achieves state-of-the-art performance on public datasets, and also demonstrates high-precision results on real-world data. The experiments with varying camera configurations validate the generalization ability and flexibility of the algorithm.