Lidar point cloud image completion method and system based on guided dynamic refinement
By employing cross-modal cross-attention feature enhancement and structure-guided dynamic iterative refinement methods, the problem of insufficient cross-modal modeling of sparse depth maps and RGB images is solved, achieving high-precision dense depth map reconstruction and improving boundary clarity and structural consistency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
In 3D scene reconstruction, existing technologies lack the ability to guide cross-modal modeling between sparse depth maps and RGB images, and the feature fusion in the decoding stage is insufficient. This results in excessive smoothing or error propagation in the depth completion results at boundaries and areas of structural abrupt changes, making it difficult to meet the requirements of high-precision scene reconstruction.
We employ a cross-modal cross-attention feature enhancement and a structure-guided dynamic iterative refinement method. By implementing fine cross-modal guidance in the encoding stage, enhancing multi-scale feature fusion in the decoding stage, and performing structure-aware refinement in the output stage, we generate a high-precision dense depth map.
It effectively improves the accuracy, boundary clarity, and structural consistency of depth completion results, and outputs dense depth maps of targets with sharp object boundaries and extremely high structural consistency.
Smart Images
Figure CN122415704A_ABST