Sem-RaDiff uses frame accumulation, a sparse coarse-to-fine network, and a diffusion model with one-step consistency sampling to generate LiDAR-like 3D semantic point clouds from mmWave radar in agricultural fields, outperforming prior radar methods on poles and wires.
Application of ai techniques and robotics in agriculture: A review,
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Sem-RaDiff: Diffusion-Based 3D Radar Semantic Perception in Cluttered Agricultural Environments
Sem-RaDiff uses frame accumulation, a sparse coarse-to-fine network, and a diffusion model with one-step consistency sampling to generate LiDAR-like 3D semantic point clouds from mmWave radar in agricultural fields, outperforming prior radar methods on poles and wires.