Search Results for author: Martin Weinmann

Found 12 papers, 3 papers with code

Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification

1 code implementation21 Jul 2022 Yongqiang Mao, Kaiqiang Chen, Wenhui Diao, Xian Sun, Xiaonan Lu, Kun fu, Martin Weinmann

With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds.

Classification Point Cloud Classification

FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery

no code implementations1 Dec 2021 Boitumelo Ruf, Martin Weinmann, Stefan Hinz

With FaSS-MVS, we present an approach for fast multi-view stereo with surface-aware Semi-Global Matching that allows for rapid depth and normal map estimation from monocular aerial video data captured by UAVs.

Depth Estimation

Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption

1 code implementation16 Jul 2021 Patrick Hübner, Martin Weinmann, Sven Wursthorn, Stefan Hinz

In this paper, we present a novel pose normalization method for indoor mapping point clouds and triangle meshes that is robust against large fractions of the indoor mapping geometries deviating from an ideal Manhattan World structure.

ReS2tAC -- UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

no code implementations15 Jun 2021 Boitumelo Ruf, Jonas Mohrs, Martin Weinmann, Stefan Hinz, Jürgen Beyerer

In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs.

FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

no code implementations9 Mar 2021 Xian Sun, Peijin Wang, Zhiyuan Yan, Feng Xu, Ruiping Wang, Wenhui Diao, Jin Chen, Jihao Li, Yingchao Feng, Tao Xu, Martin Weinmann, Stefan Hinz, Cheng Wang, Kun fu

In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15, 000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M.

Deep Learning Object +3

Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

1 code implementation17 Aug 2020 Max Hermann, Boitumelo Ruf, Martin Weinmann, Stefan Hinz

Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data.

Monocular Depth Estimation Self-Supervised Learning

Automatic Co-Registration of Aerial Imagery and Untextured Model Data Utilizing Average Shading Gradients

no code implementations26 Jun 2019 Sylvia Schmitz, Martin Weinmann, Boitumelo Ruf

These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error.

Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery

no code implementations23 Apr 2018 Boitumelo Ruf, Laurenz Thiel, Martin Weinmann

We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset.

3D Reconstruction Depth Estimation +4

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