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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. | |
| 2 | https://www.researchgate.net/publication/224643999_3D_Map_Building_Based_on_Stereo_Vision |