Learning to Remove Multipath Distortions in Time-of-Flight Range Images for a Robotic Arm Setup

TL;DR

This paper introduces a deep learning method to effectively remove multipath distortions in ToF range images captured by a robotic arm setup, significantly improving measurement accuracy.

Contribution

It presents a novel learning-based approach that automatically collects and labels training data using a structured light sensor, enabling effective multipath distortion removal.

Findings

Achieves 55% error reduction in range estimation

Outperforms baseline algorithms in experimental validation

Utilizes robotic arm for automatic data collection and labeling

Abstract

Range images captured by Time-of-Flight (ToF) cameras are corrupted with multipath distortions due to interaction between modulated light signals and scenes. The interaction is often complicated, which makes a model-based solution elusive. We propose a learning-based approach for removing the multipath distortions for a ToF camera in a robotic arm setup. Our approach is based on deep learning. We use the robotic arm to automatically collect a large amount of ToF range images containing various multipath distortions. The training images are automatically labeled by leveraging a high precision structured light sensor available only in the training time. In the test time, we apply the learned model to remove the multipath distortions. This allows our robotic arm setup to enjoy the speed and compact form of the ToF camera without compromising with its range measurement errors. We conduct extensive experimental validations and compare the proposed method to several baseline algorithms. The experiment results show that our method achieves 55% error reduction in range estimation and largely outperforms the baseline algorithms.