Segmentation-based depth correction methods for near field iToF LiDAR in motion state

This paper presents two approaches to enhance depth correction of the indirect time-of-flight (iToF) LiDAR sensors during a motion state, addressing the challenges of depth ambiguity and motion blur noise. iToF sensors are a key component in modern automotive applications, providing dense depth information for short-range vision applications for autonomous driving and Advanced Driver-Assistance Systems (ADAS). However, the periodic nature of iToF signals leads to depth ambiguity, making it challenging to measure distances accurately, especially in complex environments. Moreover, iToF sensors suffer from motion blur noise when the vehicle is in motion, compromising the accuracy of depth measurements. The proposed methods, which rely on depth correction indirectly through predicting depth bins using segmentation techniques, offer a promising alternative to direct depth regression. By focusing on segmentation-driven prediction, these new methods open up possibilities for more robust and precise depth correction in LiDAR sensor technology, potentially revolutionizing various applications that rely on accurate depth sensing. The results demonstrate the superiority of segmentation methods for depth frames based on 4 DCS samples, highlighting the potential impact and significance of this research in the field and the potential revolutionizing effect of these solutions on various applications.