面向多模态交互式融合与渐进式优化的 三维视觉理解.

3D visual understanding aims to intelligently perceive and interpret 3D scenes, achieving a profound understanding and analysis of objects, environment, and dynamic changes. As its core technology, 3D object detection plays an indispensable role. For the problem of low detection accuracy of distant targets and small targets in current 3D detection algorithms, this paper proposed a 3D object detection method called MIFPR, which was oriented towards multimodal interactive fusion and progressive refinement. In the feature extraction stage, this algorithm introduced an adaptive gated information fusion module firstly. Incorporating the geometric features of the point cloud into the image features results in a more discriminative image representation for handling variations in lighting conditions. Subsequently, the proposed voxel centroid-based deformable cross-modal attention module was to drive the fusion of rich semantic features and contextual information from images into the point cloud features. During the proposal refinement stage, this algorithm introduced a progressive attention module. By learning and aggregating features from different stages, it continuously enhanced the model’s ability to extract and model fine-grained features, progressively refining bounding boxes. This gradual refinement of the proposal helps improve the detection accuracy of distant and small objects, thereby enhancing the overall capability of visual scene understanding. The proposed method shows significant improvement in the detection accuracy of small objects like pedestrian and cyclist on the KITTI dataset compared to the state-of-the-art baseline. This confirms the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]