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February 24, 2026Smart Agricultural TechnologyOpen Access

Enhancing annotations for 5D apple pose estimation through 3D Gaussian Splatting (3DGS)

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Authors

RVRobert van de VenCentre for BioSystems GenomicsTBTrim BresillaCentre for BioSystems GenomicsBNBram NelissenCentre for BioSystems Genomics

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Implication

Novel pipeline streamlines annotations and enhances pose estimation in apples, indicating significant improvements.

Key Points

  • The research aims to enhance the efficiency and accuracy of apple pose estimation through improved annotation methods.
  • Developed a novel pipeline utilizing 3D Gaussian Splatting for scene reconstruction.
  • Automated the projection of simplified annotations to images.
  • Quantified occlusion rates during training for optimized label performance.
  • Evaluated pose estimation with various occlusion rates and dataset sizes.
  • Achieved a 99.6% reduction in manual annotations required.
  • Best performance observed with training labels from fruits ≤ 95% occluded.
  • Neutral F1 score of 0.927 on original images and 0.970 on rendered images.
  • Least occluded fruits exhibited the best position estimation accuracy.

Cite This Study

Ven et al. (2026) studied this question.

synapsesocial.com/papers/699d3fe6de8e28729cf64c7fhttps://doi.org/10.1016/j.atech.2026.101910
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