Shape Reconstruction of Foreground and Background in Scenes with Translucent Objects Based on Coding Curves

Time-of-flight (ToF) cameras, widely used in commercial applications such as augmented reality and autonomous driving, measure depth by analyzing the flight time of emitted laser signals. Indirect ToF (I-ToF) cameras are particularly popular due to their high resolution, high frame rate, and affordability. However, they struggle with depth estimation in scenes containing translucent objects, as their fundamental assumption – single direct reflectance – breaks down due to complex light interactions. In this paper, we address depth estimation with translucent objects by leveraging coding curve (CC) distortions, which have recently been shown to mitigate multi-path interference (MPI). The CC is formulated as a function derived from multiple values of various types of temporal encoding by the photodetector at each pixel. Specifically, inspired by recent MPI solutions using CC, we sample both values and depth errors under translucent objects at fixed intervals to train a model that predicts foreground and background depth errors from the CC, ensuring accurate reconstruction of translucent scenes through error correction. Our approach is validated through real-world experiments, demonstrating its effectiveness in improving depth estimation in scenes with translucent objects.