3D Vision Research

Scope of Research

The 3D vision scientific papers and articles researched are then analyzed to find how they fit in with the current computer vision architecture. The following factors are looked into and described for each study:

Problem Being Solved	What is the focus of the research paper or article? What is it trying to solve and why that is a good issue to take up?
Thought Process	How did the researchers brainstorm their solution? What were the factors taken into consideration and how did they approach the problem?
Actions Taken	What specific steps were taken to resolve the problem? How were these actions performed and why specifically these actions?
Analysis and Takeaways	What were some issues that were faced and how were they resolved? What compromises were made and how did that affect the rest of the project?
Summary and Resources	What resources can help with the research and what are the specific tools that can be used to implement this project? What are some references or documentation that can be looked into?

Goals of the 3D Vision Research Team:

Understand the current computer vision architecture thoroughly and have a high level understanding of how it integrates with the rest of the project including the firmware and electrical side. Research multiple studies on 3D vision and find key use-cases as well as benefits of incorporating this into the current computer vision architecture. Discover how and where 3D vision would fit seamlessly into the current architecture and document as much possible on the study as well as on resources that could be useful for implementing this model.

	Research Paper	Problem Being Solved	Thought Process	Actions Taken	Analysis and Takeaways	Summary and Resources
1	ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis	Hand-object pose estimation (HOPE) is extremely difficult given the different orientations and the dexterity of the human hand. ArtiBoost attempts to solve this issue.	It is an online data enhancement method that creates a CVV-space to create synthetic hand-object poses by exploration and synthesis. This is then fed into the model along with real data.	Complex statistics is involved in the creation of the CVV-space. However, the general idea is to train the model and feed the losses back to the exploration step.	The model is better performing than a dataset of only real-world hand-object poses. These synthetic hand-object poses tend to train the model better when they are more diverse rather than when in better quality.	https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.pdf