Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts
研究通过比较动力模型WRF与生成模型在不同降水极端事件中的表现，解决了次季节预报中因空间分辨率低导致的极端降水预测问题。
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UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
Existing 3D reconstruction methods struggle to simultaneously achieve photorealistic appearance, geometric accuracy, and agronomic utility in crop scenes, and lack standardized evaluation benchmarks tailored to repetitive multi-view drone imagery. This work introduces the first public benchmark dataset for precision agriculture, comprising 91 field plots of maize, soybean, wheat, and oat, along with 88,830 high-resolution RGB images. Two evaluation tracks are established: one focusing on optimized scene reconstruction using NeRF and 3D Gaussian Splatting (3DGS), and the other assessing zero-shot geometry estimation by pre-trained feedforward models such as MapAnything. Experiments show that Splatfacto-big achieves the best visual fidelity, while Scaffold-GS excels in depth and canopy height recovery; notably, only MapAnything reliably recovers absolute scale, whereas other feedforward models exhibit significant scale bias.
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Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts
研究通过比较动力模型WRF与生成模型在不同降水极端事件中的表现，解决了次季节预报中因空间分辨率低导致的极端降水预测问题。
0 citationsRead paper
UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
Existing 3D reconstruction methods struggle to simultaneously achieve photorealistic appearance, geometric accuracy, and agronomic utility in crop scenes, and lack standardized evaluation benchmarks tailored to repetitive multi-view drone imagery. This work introduces the first public benchmark dataset for precision agriculture, comprising 91 field plots of maize, soybean, wheat, and oat, along with 88,830 high-resolution RGB images. Two evaluation tracks are established: one focusing on optimized scene reconstruction using NeRF and 3D Gaussian Splatting (3DGS), and the other assessing zero-shot geometry estimation by pre-trained feedforward models such as MapAnything. Experiments show that Splatfacto-big achieves the best visual fidelity, while Scaffold-GS excels in depth and canopy height recovery; notably, only MapAnything reliably recovers absolute scale, whereas other feedforward models exhibit significant scale bias.
0 citationsRead paper