A Multi-scale Block PatchMatch-based Unified Algorithm for Efficient 6-D Vision Processing

6-D vision, which combines stereo and flow estimation to derive 3-D location and motion information, is crucial for comprehensive real-world perception. This paper presents a multi-scale block PatchMatch (MBPM)-based efficient 6-D vision algorithm with a balance tradeoff between computational complexity and accuracy, making it suitable for embedded application scenarios. By adopting a random search strategy, the proposed algorithm effectively reduces the computational burden without compromising accuracy. Additionally, the block-level processing approach further enhances efficiency by processing data in smaller, manageable chunks. Due to the significantly larger search range in flow estimation compared to stereo, we further incorporate direction initialization and multi-scale PatchMatch propagation techniques. These enhancements are crucial for ensuring reliable accuracy while expediting algorithm convergence. Compared with existing 6-D vision algorithms, the proposed method achieves 16.7% higher accuracy in stereo and 11.1% in flow estimation with much lower complexity. Even when faced with larger searching ranges, the proposed method exhibits consistent accuracy, allowing it to effectively handle fast motion scenarios.