Sungjay/3DKoreanMelon
Korean Melon 3D Growth Sequences Per-fruit 3D observation sequences of Korean melon (Cucumis melo L. var. makuwa) grown on the plant, each paired with a post-harvest scan of the same fruit and with vernier caliper measurements taken at every visit. Fruits were revisited every two to three days over a full growing period and imaged in place, so each sequence follows one identified fruit as it enlarges while foliage occludes a different part of it at each visit.… See the full description on the dataset page: https://huggingface.co/datasets/Sungjay/3DKoreanMelon.
Korean Melon 3D Growth Sequences
Per-fruit 3D observation sequences of Korean melon (Cucumis melo L. var. makuwa) grown on the plant, each paired with a post-harvest scan of the same fruit and with vernier caliper measurements taken at every visit.
Fruits were revisited every two to three days over a full growing period and imaged in place, so each sequence follows one identified fruit as it enlarges while foliage occludes a different part of it at each visit.
Layout
processed/<sample_id>/<YYYYMMDD>.ply partial point cloud, one per imaging session
gt/<sample_id>/pointcloud.ply post-harvest reference scan of the same fruit
metadata/sessions.csv one row per fruit-session, all measurements
metadata/splits.json train / validation / test fruit ids
metadata/camera/ depth and colour intrinsics, depth-to-colour extrinsics
tools/compute_volume.py recomputes harvest_volume_ml from a reference scanGreenhouse C was held out entirely for testing.
Point clouds
`processed/` — one cloud per fruit per imaging session, in metres, carrying XYZ and RGB. Each is the masked fruit region of a single RGB-D frame, back-projected with the factory intrinsics and translated so its centroid sits at the origin. These are partial views: foliage, the trellis, and the fruit's own far side are all missing, and how much is missing varies session to session.
`gt/` — one reference scan per fruit, in metres, XYZ only, recentred and aligned to a canonical orientation. Each was reconstructed from 108 images of the detached fruit on a turntable.
metadata/sessions.csv
One row per fruit-session. A session appears whether or not it produced a point cloud, so check has_pointcloud and has_caliper before reading the corresponding columns.
Only the final session has a directly measured volume; corrected_gt_ml interpolates the earlier ones from the caliper readings, so treat it as a reference rather than a measurement. Any harvest volume can be checked against the cloud it came from with python tools/compute_volume.py --all.
Loading
import open3d as o3d
import pandas as pd
sessions = pd.read_csv("metadata/sessions.csv", dtype={"session_date": str})
fruit = sessions[(sessions.sample_id == "C12") & (sessions.has_pointcloud == 1)]
for date in fruit.sort_values("session_date").session_date:
pcd = o3d.io.read_point_cloud(f"processed/C12/{date}.ply")
reference = o3d.io.read_point_cloud("gt/C12/pointcloud.ply")Collection
Grown under vertical downward training at the Seongju Korean Melon and Vegetable Research Institute, Gyeongsangbuk-do Agricultural Research and Extension Services, Republic of Korea. An Orbbec Femto Mega time-of-flight sensor on a rail-mounted platform imaged the aisles from 500–800 mm; ArUco markers in the floor tied each fruit to its plant across visits. Fruit masks were propagated with SAM 2, and depth outside 400–900 mm was discarded before back-projection. At harvest each fruit was scanned on a turntable and reconstructed with COLMAP, scaled through ArUco corner triangulation.
Both seasons are in metres, but they differ: 2025 was captured at wide field of view and gives roughly twice the points per frame; greenhouse A reference scans were resampled to 20,000 points while B and C keep their reconstructed resolution, and were cropped more aggressively near the stem. 28 fruits sharing a ground marker with another fruit have no caliper readings.
Raw RGB frames, depth maps, and masks are not included; the point clouds are the processed form the accompanying study consumed. Trained weights are released with the code.
Citation
The accompanying paper is not published yet. A preprint reference will be added here once it is posted, and replaced by the journal reference after that; until then, please cite this dataset by its DOI, 10.57967/hf/9982.
@unpublished{kim2026score,
title = {Temporal latent fusion for sequential 3D shape completion in
on-plant Korean melon growth monitoring},
author = {Kim, Sungjay and Blok, Pieter M. and Xin, Xianghui and Kim, Gyumin and
Go, Yeongjun and Ryu, Jiwon and Kim, Sang-Yeon and Lee, Chang-Hyup and
Kim, Ghiseok},
year = {2026},
note = {Manuscript in preparation}
}Code and trained weights: https://github.com/sungjay-kim/SCoRe
License
CC BY 4.0. Use it for anything, including commercially, with attribution.