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Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans
v1v2 (latest)

Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

Computer Vision and Pattern Recognition (CVPR), 2023
19 April 2023
Romain Loiseau
Elliot Vincent
Mathieu Aubry
Loic Landrieu
    3DPC
ArXiv (abs)PDFHTML

Papers citing "Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans"

3 / 3 papers shown
Title
ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds
ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds
Binbin Xiang
Maciej Wielgosz
Stefano Puliti
Kamil Král
Martin Krůček
Azim Missarov
R. Astrup
3DV
195
5
0
20 Jun 2025
Deep Learning on 3D Semantic Segmentation: A Detailed Review
Deep Learning on 3D Semantic Segmentation: A Detailed ReviewRemote Sensing (Remote Sens.), 2024
Thodoris Betsas
Andreas Georgopoulos
Anastasios Doulamis
Pierre Grussenmeyer
3DV3DPC
295
11
0
04 Nov 2024
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering
  Primitives
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering PrimitivesNeural Information Processing Systems (NeurIPS), 2023
Tom Monnier
Jake Austin
Angjoo Kanazawa
Alexei A. Efros
Mathieu Aubry
VGen
193
31
0
11 Jul 2023
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