Summary of Robust 3d Shape Reconstruction in Zero-shot From a Single Image in the Wild, by Junhyeong Cho et al.
Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild
by Junhyeong Cho, Kim Youwang, Hunmin Yang, Tae-Hyun Oh
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a unified regression model for monocular 3D shape reconstruction that addresses issues in object segmentation and occlusions. The model integrates segmentation and reconstruction tasks, enabling it to achieve state-of-the-art zero-shot results on real-world images with fewer parameters than existing methods. A scalable data synthesis pipeline is also introduced to simulate various object, occluder, and background variations for training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve monocular 3D shape reconstruction in real-world conditions by developing a new model that can handle imperfect object segmentation and occlusions. By integrating segmentation and reconstruction tasks, the proposed model achieves state-of-the-art results on real-world images using fewer parameters than existing methods. The model is trained on synthetic data simulated through a pipeline that varies objects, occluders, and backgrounds. |
Keywords
* Artificial intelligence * Regression * Synthetic data * Zero shot
