MonoSpheres achieves large-scale 3D monocular UAV exploration in unstructured real-world environments by oversampling free space in texture-sparse areas, tracking obstacle uncertainty, and using rapid replanning with perception-aware heading control.
Deep learning for monocular depth estimation: A review
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SinkSAM-Net uses topographic priors and SAM with coordinate-wise bounding box jittering to create pseudo-labels for iterative self-supervised training of an EfficientNetV2-UNet, reaching about 95% of fully supervised performance on sinkhole datasets.
citing papers explorer
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MonoSpheres: Large-Scale Monocular SLAM-Based UAV Exploration through Perception-Coupled Mapping and Planning
MonoSpheres achieves large-scale 3D monocular UAV exploration in unstructured real-world environments by oversampling free space in texture-sparse areas, tracking obstacle uncertainty, and using rapid replanning with perception-aware heading control.
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SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model
SinkSAM-Net uses topographic priors and SAM with coordinate-wise bounding box jittering to create pseudo-labels for iterative self-supervised training of an EfficientNetV2-UNet, reaching about 95% of fully supervised performance on sinkhole datasets.