Master Thesis “benchmarking of Multiview
vor 3 Wochen
Master Thesis “Benchmarking of multiview reconstruction algorithm”
- Center for Vision, Automation & Control
- Vision-based techniques and systems benefit from the availability of ground truth data sets for validation purposes and benchmarking with other techniques. Moreover, such data sets can be used for evaluating image reconstruction algorithms, to perform sanity checks during (further) development of specific vision-based techniques and systems, for the simulation of specific problems (e.g., material properties) or virtually enhancing and/or adjusting the experimental setup.
- One specific vision-based technique developed in the Competence Unit **High-Performance Vision Systems** **at the AIT is inline computational imaging (ICI), a multi-view stereo system including corresponding algorithms for 3D image reconstruction. To generate synthetic rendered image sequences and corresponding ground truth depth for ICI, a “digital twin” of a standard ICI setup has already been modeled using Blender. This “digital ICI” is controlled via in-house software, which starts the rendering processes, generates corresponding calibration data, and prepares the rendered data for further processing within our ICI pipeline. It can be adjusted to render scenes at different spatial resolutions and numbers of illuminations. Using this framework preliminary experiments were performed to compare 3D reconstructions from the ICI pipeline with ground truth data sets, but only using simple 3D objects (e.g., flat plane or ramp). This master thesis focusses on using the available “digital ICI”, and ICI 3D reconstruction algorithms to perform a more thorough evaluation. The observations made during the evaluation using ground truth data should additionally be verified in experiments using a physical ICI setup in the lab.
- The main goal of the thesis is to quantitatively evaluate current 3D reconstructions algorithms for ICI using synthetically rendered image data by our “digital ICI”, and validate the results with those obtained in the lab. This evaluation will reveal strong and weak points of current 3D reconstruction algorithms and offer a platform for identifying and planning steps for their optimization and improvement.
- To this end, the following specific aims are proposed:
- (1) Define and implement figures of merits to evaluate the accuracy of ICI 3D measurements.
- (2) Define and generate specific test cases, i.e., 3D scenes in Blender, to address various 3D reconstruction challenges (e.g., textured regions vs. low texture regions, complex vs. simple geometry, object transparency, etc.). 3D scenes do not necessarily have to be modelled from scratch but suitable 3D models generated by the Blender community may be used instead.
- (3) Validate the observations on real-world data by acquiring similar scenes using an ICI setup in the lab.
- (4) Benchmark 3D measurements of ICI using ground truth depth data and derive observations concerning ICI’s strength and weaknesses and propose a pathway for their future optimization and improvement.
- This project involves hands-on lab work, namely acquisitions of datasets as well as creative tasks, e.g., generation of testes scenes with Blender. The results of this thesis will help to optimize and improve 3D reconstruction algorithms for ICI.
- The “digital ICI” framework is the foundation for performing further evaluations, with more complex scenes, different material types and number of used illuminations, etc. This will potentially reveal insights into strengths and weaknesses of current 3D reconstruction algorithms and might lead to further developments to improve it. Particularly, as ICI relies on the presence of an object surface texture, the outcomes of this thesis will offer a platform for identifying and planning steps for the optimization and improvement of our 3D reconstruction algorithms.
- Our Competence Unit **High-Performance Vision Systems (HVS)** at AIT has been active in research for industrial inspection and quality assurance systems for over 20 years, which creates a stimulating and supportive framework for the students working with us - Therefore, you will have the unique opportunity to work on an interesting real-world problem that arose in ongoing industrial collaborations and international research projects.
Your qualifications as an
**Ingenious Partner**:
- Ongoing master’s studies in Computer Vision/Image Processing, Computer Science, Physics, Mathematics or similar
- Programming skills in Python or MATLAB expected. The project will be carried out primarily using Python
- Strong skills in image processing are expected
- Knowledge about Blender are advantageous
- Interest in technology development and applied research that is driven by real-world problems is expected
- Good communications skills and ability to work in a multidisciplinary team are necessary
- Very good command of English (in writing) is mandatory
**What to expect**:
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