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Found 11 papers, 3 papers with code
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
Ranked #1 on
Semantic Segmentation
on LaRS
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
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.
FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery
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.
Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption
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
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.
Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs
To create a 3D model of the scene, we rely on a three-stage processing chain.
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
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.
Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery
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.
Efficient Surface-Aware Semi-Global Matching with Multi-View Plane-Sweep Sampling
One key aspect in this is the efficient dense image matching and depth estimation.
Automatic Co-Registration of Aerial Imagery and Untextured Model Data Utilizing Average Shading Gradients
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
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.
