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Found 8 papers, 1 papers with code
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.
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.
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.
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV.
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.
