Spectrometer-free time-division multiplexed NIR time-of-flight vision system for visually similar material recognition - Open Access - 01.12.2026 - Article Abstract Introduction Machine vision systems have rapidly advanced across diverse fields, including automated manufacturing, robotics, and intelligent inspection. These developments have been largely driven by vision-based measurement (VBM) techniques, which integrate vision sensors, electronics, and computational algorithms1. The rise of artificial intelligence (AI), particularly machine learning (ML)-based classification and recognition methods, has significantly enhanced image analysis capabilities, improving the accuracy, reliability, and speed of measurement systems2‐7. Consequently, VBM has evolved from a simple image-processing approach into an essential tool for modern instrumentation and automation. Despite these advances, most existing vision systems still rely on RGB cameras, which provide only color and geometric information. This limits their ability to capture intrinsic material characteristics and fine surface geometry8‐11. RGB-D sensors partially alleviate this limitation by integrating active depth sensing, yet the mismatch between RGB and depth-map resolutions, along with insufficient spectral information, hinders their effectiveness in distinguishing visually similar materials12. Polarization-sensitive imaging has also been investigated as a useful modality for distinguishing visually similar materials by providing additional contrast related to scattering and surface optical properties13,14. Accurate and reliable acquisition of both material and geometric information remains a critical requirement for high-precision object recognition in production lines, recycling systems, and humanoid robotics15‐18. Anzeige The use of NIR lights (900–2500 nm) has emerged as a promising technique due to its rich material-dependent reflection and absorption features, which are more sensitive to molecular composition19‐23. Multispectral and hyperspectral imaging in the NIR range has demonstrated high effectiveness in identifying material characteristics, enabling more precise discrimination than visible-light imaging20,24‐27. However, these techniques typically rely on incoherent light sources, leading to low measurement efficiency and the inability to simultaneously capture depth information28‐31. NIR multispectral LiDAR systems have recently