Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.Comment: ECCV 201

Near Field iToF LIDAR Depth Improvement from Limited Number of Shots

Indirect Time of Flight LiDARs can indirectly calculate the scene's depth from the phase shift angle between transmitted and received laser signals with amplitudes modulated at a predefined frequency. Unfortunately, this method generates ambiguity in calculated depth when the phase shift angle value exceeds $2\pi$. Current state-of-the-art methods use raw samples generated using two distinct modulation frequencies to overcome this ambiguity problem. However, this comes at the cost of increasing laser components' stress and raising their temperature, which reduces their lifetime and increases power consumption. In our work, we study two different methods to recover the entire depth range of the LiDAR using fewer raw data sample shots from a single modulation frequency with the support of sensor's gray scale output to reduce the laser components' stress and power consumption

Time-of-Flight Cameras in Space: Pose Estimation with Deep Learning Methodologies

Recently introduced 3D Time-of-Flight (ToF) cameras have shown a huge potential for mobile robotic applications, proposing a smart and fast technology that outputs 3D point clouds, lacking however in measurement precision and robustness. With the development of this low-cost sensing hardware, 3D perception gathers more and more importance in robotics as well as in many other fields, and object registration continues to gain momentum. Registration is a transformation estimation problem between a source and a target point clouds, seeking to find the transformation that best aligns them. This work aims at building a full pipeline, from data acquisition to transformation identification, to robustly detect known objects observed by a ToF camera within a short range, estimating their 6 degrees of freedom position. We focus this work to demonstrating the capability of detecting a part of a satellite floating in space, to support in-orbit servicing missions (e.g. for space debris removal). Experiments reveal that deep learning techniques can obtain higher accuracy and robustness w.r.t. classical methods, handling significant amount of noise while still keeping real-time performance and low complexity of the models themselves