Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds

[2411.07799] Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2411.07799 (cs) [Submitted on 12 Nov 2024 ( v1 ), last revised 8 Apr 2026 (this version, v3)] Title: Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds Authors: Daniel Fusaro , Federico Magistri , Jens Behley , Alberto Pretto , Cyrill Stachniss View a PDF of the paper titled Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds, by Daniel Fusaro and 4 other authors View PDF HTML (experimental) Abstract: Accurate and consistent fruit monitoring over time is a key step toward automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. Keywords = Agricultural Robotics, 3D Fruit Tracking, Instance Segmentation, Deep Learning , Point Clouds, Sparse Convolutional Networks, Temporal Monitoring Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO) Cite as: arXiv:2411.07799 [cs.CV] (or arXiv:2411.07799v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2411.07799 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Computers and Electronics in Agriculture, Volume 247, Pages 111723, 2026 Related DOI : https://doi.org/10.1016/j.compag.2026.111723 Focus to learn more DOI(s) linking to related resources Submission history From: Daniel Fusaro [ view email ] [v1] Tue, 12 Nov 2024 13:53:22 UTC (15,128 KB) [v2] Fri, 18 Jul 2025 11:52:19 UTC (3,925 KB) [v3] Wed, 8 Apr 2026 18:44:45 UTC (8,842 KB) Full-text links: Access Paper: View a PDF of the paper titled Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds, by Daniel Fusaro and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-11 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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