Segmentation Sémantique à Grande Echelle par Graphe de Superpoints

Abstract : We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds as interconnected object parts can be efficiently captured by a structure called superpoint graph (SPG). Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets [13]), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset [2]). This is a french translation of the article [25].
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01939229
Contributor : Loic Landrieu <>
Submitted on : Thursday, November 29, 2018 - 11:58:34 AM
Last modification on : Tuesday, March 19, 2019 - 11:43:25 PM

File

RFIAP_2018_paper_54.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01939229, version 1

Collections

Citation

Loic Landrieu, Martin Simonovsky. Segmentation Sémantique à Grande Echelle par Graphe de Superpoints. RFIAP, Jun 2018, Marne-la-Vallée, France. ⟨hal-01939229⟩

Share

Metrics

Record views

67

Files downloads

111