SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation

The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white Agaricus bisporus and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1,024 points and the 6D Gram-Schmidt rotation representation yields optimal results, achieving an average rotational error of 1.67∘ on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model further generalizes well to real-world data, attaining a mean angle difference of 3.68∘ on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry.