Summary of Belhouse3d: a Benchmark Dataset For Assessing Occlusion Robustness in 3d Point Cloud Semantic Segmentation, by Umamaheswaran Raman Kumar et al.
BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation
by Umamaheswaran Raman Kumar, Abdur Razzaq Fayjie, Jurgen Hannaert, Patrick Vandewalle
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 addresses the challenge of creating large-scale datasets for machine learning in 3D vision tasks. Specifically, it focuses on indoor scene semantic segmentation, which requires real-world point clouds with ground truths. Existing synthetic datasets often fail to replicate real-world conditions, including occlusions. To overcome this limitation, the authors introduce BelHouse3D, a new synthetic point cloud dataset constructed from real-world references of 32 houses in Belgium. The dataset includes an out-of-distribution (OOD) test set simulating common occlusions in real-world point clouds. The authors evaluate popular point-based semantic segmentation methods using their OOD setting and present a benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by making it easier to train them for 3D tasks like recognizing objects inside buildings. It’s hard to collect and label the data needed for this because it requires going into spaces and labeling every single point on an object. To solve this problem, researchers created a new dataset called BelHouse3D that mimics real-world conditions by using pictures of 32 houses in Belgium as references. The dataset also includes fake data with missing information to test how well machines can handle unexpected situations. By testing different machine learning models on this dataset, the researchers hope to find better ways to train machines for 3D tasks. |
Keywords
» Artificial intelligence » Machine learning » Semantic segmentation
