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SuP-SLiP: Subsampled Processing of Large-scale Static LIDAR Point Clouds

Published: 29 October 2024 Publication History

Abstract

Annotation is a crucial component of point cloud analysis. However, due to the sheer number of points in large-scale static point clouds, it is an expensive and time-consuming process. We address this issue using a novel lightweight approach to reduce the overall annotation time for unlabelled static point clouds in the binary road segmentation task. By leveraging models trained on other point clouds in the same distribution, and radius sampling, our approach determines a small fraction of points for annotation. It automatically labels the remaining points using nearest-neighbor aggregation. We implement this approach in an end-to-end system for mobile laser scanning (MLS) or mobile LiDAR point clouds, SuP-SLiP, i.e., Subsampled Processing of Large-scale Static LIDAR Point Clouds. We validate the robustness of this method through the bit-flipping adversarial attack and account for varying budgets by providing a feature that suggests a custom number of points to annotate for a given point cloud.

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cover image ACM Conferences
GeoSearch '24: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data
October 2024
53 pages
ISBN:9798400711480
DOI:10.1145/3681769
Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 29 October 2024

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Author Tags

  1. Adversarial attack
  2. Annotation
  3. Binary classification
  4. Bit flipping
  5. LIDAR point clouds
  6. Nearest neighbors
  7. Point cloud processing
  8. Road segmentation
  9. Statistical analysis

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