Cross-spectral Gated-RGB Stereo Depth Estimation

Abstract

Gated cameras flood-illuminate a scene and capture the time-gated impulse response of a scene. By employing nanosecond-scale gates, existing sensors are capable of capturing mega-pixel gated images, delivering dense depth improving on today's LiDAR sensors in spatial resolution and depth precision. Although gated depth estimation methods deliver a million of depth estimates per frame, their res-olution is still an order below existing RGB imaging methods. In this work, we combine high-resolution stereo HDR RCCB cameras with gated imaging, allowing us to exploit depth cues from active gating, multi-view RGB and multi-view NIR sensing - multi-view and gated cues across the entire spectrum. The resulting capture system consists only of low-cost CMOS sensors and flood-illumination. We pro-pose a novel stereo-depth estimation method that is capa-ble of exploiting these multi-modal multi-view depth cues, including the active illumination that is measured by the RCCB camera when removing the IR-cut filter. The pro-posed method achieves accurate depth at long ranges, out-performing the next best existing method by 39% for ranges of 100 to 220 m in MAE on accumulated LiDAR ground-truth. Our code, models and datasets are available here11https://light.princeton.edu/gatedrccbstereo/.