Optimizing LiDAR-Based Depth Map Accuracy Through Multiview Angle Analysis to Enhance Shape Measurement Efficiency

This study introduces an advanced methodology to overcome the intrinsic challenges associated with Light Detection and Ranging (LiDAR) technology integrated into Apple's devices, specifically focusing on optimizing depth map accuracy through multiview angle analysis. Despite the extensive utility of LiDAR in generating detailed three-dimensional models for various applications, from autonomous vehicle navigation to environmental conservation, the efficiency of the technology is often compromised by difficulties in accurately rendering complex geometries from multiple perspectives and by its substantial demands on computational resources. To address these limitations, depth map data are systematically collected and evaluated from four distinct viewing angles (0, 90, 180, and 270 degrees) against established ground truth benchmarks. With a threshold of Intersection over Union (IoU) set at 0.8, it is inferred that the predicted shape exhibits over 80% similarity to the actual shape. When only one view is utilized, an IoU of 0.95 is achieved, alongside a size reduction of 73.07%, a Perimeter Accuracy reduction of 1.56%, and an increase in Root Mean Square Error (RMSE) of 29.37%. This comprehensive analysis highlights the critical need for precise geometric representation and marks a significant step toward enhancing the fidelity of LiDAR measurements. The results of this research indicate a substantial improvement in the accuracy of three-dimensional representations, suggesting a potential shift in the utilization of LiDAR technology across various scientific and infrastructural domains. By proposing a novel framework that optimizes data precision and processing efficiency, this paper contributes to the ongoing discourse on improving 3D modeling and analysis techniques.