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Efficient LiDAR data compression for embedded V2I or V2V data handling

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Paul Caillet, Yohan Dupuis

Robotics · Submitted 11 Apr 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper we present a method that can be used to efficiently compress LiDAR data in order to facilitate storage and transmission in V2V or V2I applications.

how it is stated · asserted 0.81 read the record →

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arXiv ID1904.05649 (version 1)
Pith IDTV3ZAC6D, Pith's permanent record of this paper and its review
CitationPaul Caillet, Yohan Dupuis. "Efficient LiDAR data compression for embedded V2I or V2V data handling." arXiv:1904.05649 (2019). https://pith.science/paper/TV3ZAC6D
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,833 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.14 · human participants 0.02 · controlled comparison 0.68 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.97 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

LiDAR are increasingly being used in intelligent vehicles (IV) or intelligent transportation systems (ITS). Storage and transmission of data generated by LiDAR sensors are one of the most challenging aspects of their deployment. In this paper we present a method that can be used to efficiently compress LiDAR data in order to facilitate storage and transmission in V2V or V2I applications. This method can be used to perform lossless or lossy compression and is specifically designed for embedded applications with low processing power. This method is also designed to be easily applicable to existing processing chains by keeping the structure of the data stream intact. We benchmarked our method using several publicly available datasets and compared it with state-of-the-art LiDAR data compression methods from the literature.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 LiDAR are increasingly being used in intelligent vehicles (IV) or intelligent transportation systems (ITS). background 1.00 0.05 0.02 asserted 0.98
s1 Storage and transmission of data generated by LiDAR sensors are one of the most challenging aspects of their deployment. background 0.97 0.08 0.02 asserted 1.00
s2 In this paper we present a method that can be used to efficiently compress LiDAR data in order to facilitate storage and transmission in V2V or V2I applications. contribution 1.00 0.87 0.39 asserted 0.81
s3 This method can be used to perform lossless or lossy compression and is specifically designed for embedded applications with low processing power. contribution 0.54 0.62 0.12 asserted 0.97
s4 This method is also designed to be easily applicable to existing processing chains by keeping the structure of the data stream intact. contribution 0.58 0.43 0.06 asserted 1.00
s5 We benchmarked our method using several publicly available datasets and compared it with state-of-the-art LiDAR data compression methods from the literature. method 0.88 0.96 0.08 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.59.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

LiDAR are increasingly being used in intelligent vehicles (IV) or intelligent transportation systems (ITS). Storage and transmission of data generated by LiDAR sensors are one of the most challenging aspects of their deployment. In this paper we present a method that can be used to efficiently compress LiDAR data in order to facilitate storage and transmission in V2V or V2I applications. This method can be used to perform lossless or lossy compression and is specifically designed for embedded applications with low processing power. This method is also designed to be easily applicable to existing processing chains by keeping the structure of the data stream intact. We benchmarked our method using several publicly available datasets and compared it with state-of-the-art LiDAR data compression methods from the literature.

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