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.

Fruits	210
Partial point clouds	2,738 (1–16 sessions per fruit, 13 on average)
Sessions with caliper readings	2,312
Post-harvest reference scans	210
Seasons / greenhouses / cultivars	2 / 3 / 2
Size	~334 MB

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 scan

Prefix	Greenhouse	Imaging window	Cultivar	Fruits	Split
A1–A61	A	2025, 1–29 May	Eliteggul	61	train, validation
B1–B69	B	2026, 11 Mar – 16 Apr	Alchanggul	69	train, validation
C1–C80	C	2026, 11 Mar – 16 Apr	Alchanggul	80	test

Greenhouse 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.

Column	Meaning
sample_id	public identifier, e.g. C12
source_id	identifier used in the accompanying code
greenhouse, season, cultivar, split	per-fruit attributes, repeated on each row
session_date	imaging date, YYYYMMDD
is_last_session	1 on the final session, the one nearest harvest
has_pointcloud	1 if processed/<sample_id>/<session_date>.ply exists
n_points	points in that cloud
passes_quality_filter	1 if n_points >= 300, the threshold used in the accompanying study
has_caliper	1 if the fruit was measured at this session
height_mm, width_mm	vernier caliper readings
ellipsoid_ml	prolate-ellipsoid volume from those two readings
corrected_gt_ml	ellipsoid_ml scaled by alpha_correction, an interpolated size reference for sessions before harvest
harvest_volume_ml	volume of the post-harvest scan, as the convex hull of gt/<sample_id>/pointcloud.ply
harvest_mass_g	mass at harvest, where recorded
alpha_correction	harvest_volume_ml divided by the final ellipsoid_ml
n_sessions	sessions of this fruit that produced a point cloud

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.