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arXiv Computer Vision By Woojung Son (Department of Agricultural and Biological Engineering, University of Florida), Won Suk Lee (Department of Agricultural and Biological Engineering, University of Florida), Zijing Huang (Department of Agricultural and Biological Engineering, University of Florida), Daeun Choi (Department of Agricultural and Biological Engineering, University of Florida), Catia Silva (Department of Electrical and Computer Engineering, University of Florida), Yu She (Edwardson School of Industrial Engineering, Purdue University), Yan Gu (School of Mechanical Engineering, Purdue University)

From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting

Read the original on arXiv Computer Vision →

The paper presents the first real‑world 6D pose ground‑truth dataset for red‑stage strawberries, collected from 12,040 images at an actual farm using indirect camera pose recovery and 3D bounding‑box annotation. It also introduces a synthetic dataset rendered in NVIDIA Isaac Sim with scene‑level realism and domain randomization. Experiments show that models trained solely on synthetic data do not transfer well to in‑field images, but adding a small amount of real data significantly improves both translation and rotation accuracy across various backbone encoders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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