A light transport model for mitigating multipath interference in Time-of-flight sensors

Abstract

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI). Specifically, MPI occurs when multiple optical reflections return to a single spatial location on the imaging sensor. Many prior approaches to rectifying MPI rely on sparsity in optical reflections, which is an extreme simplification. In this paper, we correct MPI by combining the standard measurements from a TOF camera with information from direct and global light transport. We report results on both simulated experiments and physical experiments (using the Kinect sensor). Our results, evaluated against ground truth, demonstrate a quantitative improvement in depth accuracy.

DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

“…Depth refinement for indoor environment. In the indoor environment, the quality of the depth from commodity RGB-D sensors is not ideal due to the limitation of the sensing technologies [3,37,11]. A lot of works have been proposed to improve the depth using an aligned highresolution color image.…”

Polarized 3D: High-Quality Depth Sensing with Polarization Cues

“…The proposed technique of polarization enhancement drastically outperforms a state-of-the-art technique for multipath correction, while using fewer images [29]. Refer to the caption of Fig.…”

“…The coarse depth map contains quantization errors and noise, so Figure 3. A commonly used benchmark scene [13,29]. Combining polarization with Kinect results in improved performance.…”

“…Robustness to diffuse multipath: Diffuse multipath has been an active challenge in the ToF community [35,13,29]. The proposed technique of polarization enhancement drastically outperforms a state-of-the-art technique for multipath correction, while using fewer images [29].…”

Direct Object Recognition Without Line-Of-Sight Using Optical Coherence

“…This aspect allows our method to overcome some of the critical practical limitations of the related imaging methods. The proposed method requires hardware (mostly consumer-grade electronics) that is far less expensive than that required for the TOF-based NLOS imaging [16,24,25,26,27,33,37,39,40], and the method is more robust than the memoryeffect based imaging techniques that have a limited field-ofview [11,21]. Moreover, a recent publication [31] also uses only ordinal digital cameras but would require very specific scene setup (an accidental occlusion) to obtain better performance.…”