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FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-based Point Clouds

Published: 20 October 2020 Publication History

Abstract

Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. In addition, we carefully design the UI for different stages of the annotation procedure, thus keeping the annotators to focus on the aspects that are most important to each stage. Furthermore, our system also greatly reduces the amount of interaction by introducing a lightweight yet effective mechanism to propagate the annotation results. Experimental results show that our method can remarkably accelerate the procedure and improve the annotation quality.

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References

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Cited By

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  • (2024)Click, Crop & Detect: One-Click Offline Annotation for Human-in-the-Loop 3D Object Detection on Point Clouds2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00454(4514-4525)Online publication date: 17-Jun-2024
  • (2023)PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds2023 18th International Conference on Machine Vision and Applications (MVA)10.23919/MVA57639.2023.10216156(1-5)Online publication date: 23-Jul-2023

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Published In

cover image ACM Conferences
UIST '20 Adjunct: Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology
October 2020
203 pages
ISBN:9781450375153
DOI:10.1145/3379350
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 20 October 2020

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Author Tags

  1. autonomous driving
  2. lidar
  3. point cloud annotation
  4. scene understanding

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  • Poster

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  • SenseTime Collaborative Grant

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UIST '20

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Overall Acceptance Rate 355 of 1,733 submissions, 20%

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UIST '25
The 38th Annual ACM Symposium on User Interface Software and Technology
September 28 - October 1, 2025
Busan , Republic of Korea

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Cited By

View all
  • (2024)Click, Crop & Detect: One-Click Offline Annotation for Human-in-the-Loop 3D Object Detection on Point Clouds2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00454(4514-4525)Online publication date: 17-Jun-2024
  • (2023)PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds2023 18th International Conference on Machine Vision and Applications (MVA)10.23919/MVA57639.2023.10216156(1-5)Online publication date: 23-Jul-2023

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