Summary of Open-ended 3d Point Cloud Instance Segmentation, by Phuc D.a. Nguyen et al.
Open-Ended 3D Point Cloud Instance Segmentation
by Phuc D.A. Nguyen, Minh Luu, Anh Tran, Cuong Pham, Khoi Nguyen
First submitted to arxiv on: 21 Aug 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 The proposed Open-Ended 3D Instance Segmentation (OE-3DIS) method eliminates the need for predefined class names during testing, allowing agents to operate autonomously. This is achieved by leveraging 2D Multimodal Large Language Models and a set of strong baselines derived from OV-3DIS approaches. The OE-3DIS system introduces a novel Open-Ended score that evaluates both semantic and geometric quality of predicted masks and their associated class names, alongside the standard AP score. The approach demonstrates significant performance improvements on ScanNet200 and ScanNet++ datasets, surpassing the current state-of-the-art OV-3DIS method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to identify objects in 3D space without needing to know what those objects are called beforehand. This is important because it allows machines to operate independently. The authors use special language models and test their approach on two big datasets. They also introduce a new way to measure how well the approach works, which takes into account both what the object looks like and what it’s called. The results show that this approach is better than previous ones at doing this task. |
Keywords
» Artificial intelligence » Instance segmentation
