Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

A variety of techniques such as light field, structured illumination, and time-of-flight (TOF) are commonly used for depth acquisition in consumer imaging, robotics and many other applications. Unfortunately, each technique suffers from its individual limitations preventing robust depth sensing. In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor. We refer to this combination as depth field imaging. Depth fields combine light field advantages such as synthetic aperture refocusing with TOF imaging advantages such as high depth resolution and coded signal processing to resolve multipath interference. We show applications including synthesizing virtual apertures for TOF imaging, improved depth mapping through partial and scattering occluders, and single frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding, depth fields can improve depth sensing in the wild and generate new insights into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201

Robot guidance using machine vision techniques in industrial environments: A comparative review

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works

What Is Around The Camera?

How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror. An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance.Comment: Accepted to ICCV. Project: http://homes.esat.kuleuven.be/~sgeorgou/multinatillum

Review of post-process optical form metrology for industrial-grade metal additive manufactured parts

The scope of this review is to investigate the main post-process optical form measurement technologies available in industry today and to determine whether they are applicable to industrial-grade metal additive manufactured parts. An in-depth review of the operation of optical three-dimensional form measurement technologies applicable to metal additive manufacturing is presented, with a focus on their fundamental limitations. Looking into the future, some alternative candidate measurement technologies potentially applicable to metal additive manufacturing will be discussed, which either provide higher accuracy than currently available techniques but lack measurement volume, or inversely, which operate in the appropriate measurement volume but are not currently accurate enough to be used for industrial measurement

Acquisition of 3D shapes of moving objects using fringe projection profilometry

Three-dimensional (3D) shape measurement for object surface reconstruction has potential applications in many areas, such as security, manufacturing and entertainment. As an effective non-contact technique for 3D shape measurements, fringe projection profilometry (FPP) has attracted significant research interests because of its high measurement speed, high measurement accuracy and ease to implement. Conventional FPP analysis approaches are applicable to the calculation of phase differences for static objects. However, 3D shape measurement for dynamic objects remains a challenging task, although they are highly demanded in many applications. The study of this thesis work aims to enhance the measurement accuracy of the FPP techniques for the 3D shape of objects subject to movement in the 3D space. The 3D movement of objects changes not only the position of the object but also the height information with respect to the measurement system, resulting in motion-induced errors with the use of existing FPP technology. The thesis presents the work conducted for solutions of this challenging problem