Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation Article Swipe
Hanieh Shojaei Miandashti
,
Qianqian Zou
,
Claus Brenner
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.11935
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.11935
Reliable deep learning models require not only accurate predictions but also well-calibrated confidence estimates to ensure dependable uncertainty estimation. This is crucial in safety-critical applications like autonomous driving, which depend on rapid and precise semantic segmentation of LiDAR point clouds for real-time 3D scene understanding. In this work, we introduce a sampling-free approach for estimating well-calibrated confidence values for classification tasks, achieving alignment with true classification accuracy and significantly reducing inference time compared to sampling-based methods. Our evaluation using the Adaptive Calibration Error (ACE) metric for LiDAR semantic segmentation shows that our approach maintains well-calibrated confidence values while achieving increased processing speed compared to a sampling baseline. Additionally, reliability diagrams reveal that our method produces underconfidence rather than overconfident predictions, an advantage for safety-critical applications. Our sampling-free approach offers well-calibrated and time-efficient predictions for LiDAR scene semantic segmentation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.11935
- https://arxiv.org/pdf/2411.11935
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404573375
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404573375Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.11935Digital Object Identifier
- Title
-
Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic SegmentationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-18Full publication date if available
- Authors
-
Hanieh Shojaei Miandashti, Qianqian Zou, Claus BrennerList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.11935Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.11935Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2411.11935Direct OA link when available
- Concepts
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Lidar, Segmentation, Computer science, Artificial intelligence, Estimation, Sampling (signal processing), Computer vision, Statistics, Remote sensing, Geography, Mathematics, Engineering, Filter (signal processing), Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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