Summary of Paris3d: Reasoning-based 3d Part Segmentation Using Large Multimodal Model, by Amrin Kareem et al.
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
by Amrin Kareem, Jean Lahoud, Hisham Cholakkal
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel segmentation task for 3D objects is introduced, focusing on reasoning part segmentation based on implicit textual queries. A large dataset with over 60k instructions and ground-truth annotations is presented to facilitate evaluation and benchmarking. A proposed model can segment parts of 3D objects based on implicit textual queries and generate natural language explanations. The method achieves competitive performance to models using explicit queries, with added abilities for identifying part concepts, reasoning about them, and incorporating world knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to segment parts of 3D objects using complex and implicit textual queries. Think of it like asking Alexa to find the wheels on your car or the engine on a bike. The researchers created a big dataset with lots of examples and a special model that can understand these types of requests and explain its answers in simple language. This is important because it lets computers work more like humans, using natural language to communicate. |
