Centimeter-wave Free-space Neural Time-of-Flight Imaging

Abstract

Depth sensors have emerged as a cornerstone sensor modality with diverse applications in personal hand-held devices, robotics, scientific imaging, autonomous vehicles, and more. In particular, correlation Time-of-Flight (ToF) sensors have found widespread adoption for meter-scale indoor applications such as object tracking and pose estimation. While they offer high depth resolution at competitive costs, the precision of these indirect ToF sensors is fundamentally limited by their modulation contrast, which is in turn limited by the effects of photo-conversion noise. In contrast, optical interferometric methods can leverage short illumination modulation wavelengths to achieve depth precision three orders of magnitude greater than ToF, but typically find their range restricted to the sub-centimeter. In this work, we merge concepts from both correlation ToF design and interferometric imaging; a step towards bridging the gap between these methods. We propose a computational ToF imaging method which optically computes the GHz ToF correlation signal in free space before photo-conversion. To acquire a depth map, we scan a scene point-wise and computationally unwrap the collected correlation measurements. Specifically, we repurpose electro-optical modulators used in optical communication for ToF imaging with centimeter-wave signals, and achieve all-optical correlation at 7.15 and 14.32 GHz modulation frequencies. While GHz modulation frequencies increase depth precision, these high modulation rates also pose a technical challenge. They result in dozens of wraps per meter which cannot be estimated robustly by existing phase unwrapping methods. We tackle this problem with a proposed segmentation-inspired phase unwrapping network, which exploits the correlation of adjacent GHz phase measurements to classify regions into their respective wrap counts. We validate this method in simulation and experimentally, and demonstrate precise depth sensing using centimeter wave modulation that is robust to surface texture and ambient light. Compared to existing analog demodulation methods, the proposed system outperforms all of them across all tested scenarios.