Shay85Gil/somethings_missing_here

Something's Missing Here

A dataset of viewpoint-aligned before/after image pairs for training and evaluating missing-object detection: each pair shows the same scene from (nearly) the same viewpoint, once with one or more objects present and once after they were removed, with a label naming what went missing.

The dataset originated from pairs derived from the simkoc/Remove360 dataset (training/Remove360_based/), extended with original photographs collected by the dataset author (training/DeTaken/), plus a held-out test/ split of scenes that appear nowhere in training. A separate challenging/ split holds harder cases — object swaps, replacements, and rearrangements — where telling "something is missing" apart from "things merely changed" takes more than spotting a difference (see below).

Structure

Both splits are organized by collection, then scene, then pair:

training/
├── DeTaken/                 original photographs by the dataset author
│   └── <scene>/             boxes, cabinet_window, magnets, red_stool, ...
│       └── pair_<nn>/
│           ├── before.jpg   earlier image
│           ├── after.jpg    later image
│           └── label.json   {"missing": true, "items": ["<removed object>"]}
│                            or {"missing": false, "items": []} for no-change pairs
└── Remove360_based/         pairs derived from Remove360 (see below)
    └── <scene>/             backyard_big_tree, backyard_bricks, backyard_toys,
        └── pair_<nn>/       bedroom, living-room, office, park, stairwell
test/
├── DeTaken/                 held-out scenes, never in training
│   └── test_<scene>/        test_broom, test_chair, ... (one pair each;
│       └── pair_<nn>/       test_michal_01: 15 pairs, test_shay_01: 11)
└── Remove360_based/
    └── test_backyard/       3 held-out Remove360 pairs
challenging/                 harder cases: swaps, replacements, rearrangements
├── training/
│   └── <scene>/             same scene names as training/DeTaken
│       └── pair_<nn>/
└── test/
    └── pair_<nn>/           held-out challenging pairs (flat)

Overview:

split	collection	scenes	pairs	positive	negative
training	DeTaken	16	61	51	10
training	Remove360_based	8	143	121	22
test	DeTaken	10	34	32	2
test	Remove360_based	1	3	3	0
challenging	training	17	81	27	54
challenging	test	1	11	5	6

Remove360-derived negative (no-change) pairs additionally carry a source field in label.json documenting which images they were built from.

In Remove360_based/ pairs (both splits), before.jpg is warped into after.jpg's camera frame and both images are cropped to their shared valid region, so the two images are pixel-aligned with identical dimensions. DeTaken/ and challenging/ pairs are handheld re-shots from approximately the same viewpoint and are not pixel-aligned.

The challenging split

challenging/ holds the deliberately hard cases, separated from the main splits so models can be trained and evaluated with or without them. In the main splits, the change between before and after is a clean object removal (or nothing at all) seen under a small viewpoint change. In challenging/, other things happen too — objects are swapped with one another, replaced by different objects, or rearranged within the scene:

• Its negatives (missing: false, the majority here) are hard: items moved, swapped, or substituted — the scene visibly changed, yet nothing went missing. A model leaning on "the images differ, so something is gone" fails these.
• Its positives (missing: true) hide a genuine removal among such distractions, so spotting the difference is not enough — the model must identify that the change is specifically a disappearance.

Labels use the same label.json format. challenging/test/ pairs are held out from all training and sit directly under the folder (no scene subfolders).

The Remove360_based subset

Remove360 provides separate pre-removal and post-removal camera walks of real indoor and outdoor scenes. Its before and after images are independent captures — they are not pixel-aligned pairs — so this subset was built by finding and aligning the closest matching viewpoints between the two walks.

146 pairs — 124 positives (something was removed) and 22 negatives (nothing changed) — across 9 scenes and 9 removed objects. Remove360's single large backyard scene is split into three sub-scenes by area (big tree lawn, brick patio, toy corner), and 3 backyard pairs are held out as test/ Remove360_based/test_backyard:

scene	positive	negative		removed object	pairs
backyard_toys	23	5		chairs	36
backyard_bricks	16	0		backpack	24
backyard_big_tree	11	2		stroller	16
stairwell	24	7		sofa	15
living-room	21	5		deckchair	11
office	13	1		bicycle	10
park	10	2		pillows	6
bedroom	3	0		table	3
test_backyard (test)	3	0		toy-truck	3

How it was generated

1. Download — the full simkoc/Remove360 repository (file tree of <scene>/<object>/train|test|masks), where train/ holds pre-removal ("before") images and test/ holds post-removal ("after") images.
2. Valid-region cropping — a subset of Remove360's images is truncated at fixed byte boundaries on the Hub itself (all of backyard/stroller at 2.75 MiB, all of backyard/playhouse at 256 KiB); truncated JPEGs decode with a uniform gray tail. Each image was cropped to its real content before matching, and images with less than 15% real content were discarded.
3. Viewpoint matching — every after image was ranked against all before images of the same object by SIFT feature matches (Lowe ratio 0.75); the top 3 candidates were verified with a RANSAC homography (reprojection threshold 4 px, minimum 40 inliers).
4. Acceptance criteria — a pair was kept only if each frame covers at least 85% of the other under the homography (mutual frame coverage) and the warped before image correlates with the after image at ≥ 0.475 zero-mean normalized correlation. Each before image was used in at most one pair.
5. Alignment and cropping — the accepted before image was warped into the after frame at full resolution and both images were cropped to the largest rectangle of shared valid pixels.
6. Labeling — each pair's label.json records the removed object (the Remove360 object folder the pair came from) as {"missing": true, "items": ["<object>"]}.
7. Manual curation — the automatically accepted pairs were reviewed and some were deleted by hand; the remaining pairs were renumbered contiguously.
8. No-change negatives — within one scene, the before images of two different pairs that share the same removed-item label come from the same pre-removal camera walk (the item is present in both), and likewise the after images from the same post-removal walk (absent in both). Such same-side image pairs were matched and aligned with the same gates as the positives — with mutual frame coverage additionally capped at 96% so overlap statistics cannot separate the classes — and saved as {"missing": false} pairs.

Known limitations

• No playhouse pairs — all of Remove360's backyard/playhouse images are truncated to ~6% of their content on the Hub, which is below the usability floor.
• Stroller pairs are half-height — backyard/stroller images are truncated to roughly their top half, so its pairs are wide bands (3900×1000) rather than full frames (4000×2200).
• Residual parallax — alignment uses a single homography per pair; small parallax between the two camera positions can remain, especially on close foreground geometry.
• Some objects other than the labeled one may have shifted slightly between Remove360's two capture sessions.

License and attribution

The Remove360_based/ subset is a derivative of simkoc/Remove360 and is distributed under the same CC-BY-NC-4.0 license, which this dataset adopts as a whole. If you use it, please also cite the original Remove360 paper (arXiv:2508.11431).