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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:
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. Key Requirements:
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Research Paper | Problem Being Solved | Thought Process | Actions Taken | Analysis and Takeaways | Summary and Resources | |
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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. |