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Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discussExpand
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Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is aExpand
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A Higher-Order CRF Model for Road Network Extraction
The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form a connected network of smooth, thinExpand
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This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images,Expand
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Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees
Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of denselyExpand
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Contour Detection in Unstructured 3D Point Clouds
We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediateExpand
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Learning Aerial Image Segmentation From Online Maps
This paper deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic mapExpand
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Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-resolution Model for Multi-class Volumetric Labeling
We propose an adaptive multi-resolution formulation of semantic 3D reconstruction. Given a set of images of a scene, semantic 3D reconstruction aims to densely reconstruct both the 3D shape of theExpand
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Mind the Gap: Modeling Local and Global Context in (Road) Networks
We propose a method to label roads in aerial images and extract a topologically correct road network. Three factors make road extraction difficult: (i) high intra-class variability due to clutterExpand
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Conditional Random Fields for Urban Scene Classification with Full Waveform LiDAR Data
We propose a context-based classification method for point clouds acquired by full waveform airborne laser scanners. As these devices provide a higher point density and additional information likeExpand
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