Transferring facade labels between point clouds with semantic octrees while considering change detection

2024-02-09 · Sophia Schwarz, Tanja Pilz, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality. However, simply having this data is not enough; to extract useful information, semantic labeling is crucial. In this context, we propose a method to transfer annotations from a labeled to an unlabeled point cloud using an octree structure. The structure also analyses changes between the point clouds. Our experiments confirm that our method effectively transfers annotations while addressing changes. The primary contribution of this project is the development of the method for automatic label transfer between two different point clouds that represent the same real-world object. The proposed method can be of great importance for data-driven deep learning algorithms as it can also allow circumventing stochastic transfer learning by deterministic label transfer between datasets depicting the same objects.

📄 PDF Abstract BibTeX arXiv:2402.06531

Code (1)

schwarzsophia/transferring_urban_labels_between_pointclouds 공식 구현

Tasks

Change Detection Transfer Learning

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Thermal point clouds integrate thermal radiation and laser point clouds effectively. However, the semantic information for the interpretation of building thermal point clouds can hardly be precisely inferred. Transferrin…

Position

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3D Semantic Segmentation Decoder Point Cloud Segmentation Segmentation+1

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3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have …

Classification Deep Learning Point Cloud Classification Semantic Segmentation

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2013-08-29 · Karim Hammoudi, Fadi Dornaika, Bahman Soheilian, Bruno Vallet 외

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