Search Results for author:
Found 23 papers, 10 papers with code
A Higher-Order CRF Model for Road Network Extraction
The aim of this work is to extract the road network from aerial images.
Fast semantic segmentation of 3d point clouds with strongly varying density
We describe an effective and efficient method for point-wise semantic classification of 3D point clouds.
Ranked #17 on
Semantic Segmentation
on Semantic3D
Contour Detection in Unstructured 3D Point Clouds
The contour scores serve as a basis to construct an overcomplete graph of candidate contours.
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.
Cataloging Public Objects Using Aerial and Street-Level Images - Urban Trees
The main technical challenge is combining test time information from multiple views of each geographic location (e. g., aerial and street views).
Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks.
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes.
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks
While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data.
Counting the uncountable: deep semantic density estimation from Space
This task is hard due to the cluttered nature of scenes where different object categories occur.
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Ranked #4 on
Point Cloud Registration
on ETH (trained on 3DMatch)
Simultaneous multi-view instance detection with learned geometric soft-constraints
We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection.
Learning multiview 3D point cloud registration
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end
In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
Mapping oil palm density at country scale: An active learning approach
To that end, we propose a new, active deep learning method to estimate oil palm density at large scale from Sentinel-2 satellite images, and apply it to generate complete maps for Malaysia and Indonesia.
Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.
Learning Graph Regularisation for Guided Super-Resolution
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
Recognition of Unseen Bird Species by Learning from Field Guides
Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
Satellite-based high-resolution maps of cocoa planted area for Côte d'Ivoire and Ghana
C\^ote d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production.
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation
Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation.
BiasBed -- Rigorous Texture Bias Evaluation
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
BiasBed - Rigorous Texture Bias Evaluation
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain
Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.
GeoGraph: Graph-based multi-view object detection with geometric cues end-to-end
In this paper, we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
