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Datasets:
IRVLUTD
/
RPX

Tasks:
Image Segmentation
Depth Estimation
Object Detection
Modalities:
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Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
robotics
embodied-ai
rgb-d
benchmark
perception
manipulation
Libraries:
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pandas
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License:
Dataset card Data Studio Files
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Narendhiranv04 commited on
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Add RPX dataset card, Croissant metadata, and splits

Browse files
Files changed (6) hide show
  1. README.md +212 -0
  2. rpx_croissant.json +274 -0
  3. splits/easy.txt +100 -0
  4. splits/hard.txt +100 -0
  5. splits/medium.txt +100 -0
  6. splits/scene_splits.json +2094 -0
README.md ADDED
@@ -0,0 +1,212 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: cc-by-4.0
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+ pretty_name: "RPX: Robot Perception X"
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+ task_categories:
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+ - image-segmentation
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+ - depth-estimation
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+ - object-detection
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+ - visual-question-answering
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+ language:
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+ - en
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+ tags:
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+ - robotics
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+ - embodied-ai
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+ - rgb-d
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+ - benchmark
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+ - perception
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+ - manipulation
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+ - stereo
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+ size_categories:
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+ - 100B<n<1T
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+ configs:
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+ - config_name: multi_object
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+ description: "MOS scenes (3 phases each), tertile-cut by Effort-Stratified Difficulty."
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+ data_files:
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+ - split: "easy"
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+ path: "splits/easy.txt"
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+ - split: "medium"
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+ path: "splits/medium.txt"
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+ - split: "hard"
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+ path: "splits/hard.txt"
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+ - config_name: single_object
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+ description: "SOS scenes (one 360° collection per object); no difficulty split."
33
+ ---
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+
35
+ # RPX: Robot Perception X
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+
37
+ A real-world RGB-D benchmark for evaluating robot perception under
38
+ embodied deployment conditions.
39
+
40
+ * Code: https://github.com/IRVLUTD/RPX
41
+
42
+ ![RPX teaser](assets/rpx_teaser.png)
43
+
44
+ [Watch the RPX teaser video](assets/rpx-jumbotron.webm)
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+
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+ ## Dataset at a glance
47
+
48
+ | | |
49
+ |---|---|
50
+ | Multi-object scenes (MOS) | **100** (`scene001` to `scene100`, 3 phases each: clutter / interaction / clean) |
51
+ | Single-object scenes (SOS) | **70 selected objects** (one 360 collection per object) |
52
+ | Frame manifest rows | **110,000** (75,000 MOS + 35,000 selected SOS) |
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+ | MOS mask-object rows | **2,100** local mask IDs mapped to global object IDs |
54
+
55
+ Scene renames are documented in `manifest/scene_name_mapping_v1.csv`,
56
+ mapping original scene names to `scene001` through `scene100`.
57
+
58
+ ## Modality inventory
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+
60
+ The table below describes this cleaned release. `cam_pose_icp` is not included in this cleaned release.
61
+ Use `manifest/frames_v1.parquet` and the identity manifests under `manifest/`
62
+ as the source of truth.
63
+
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+ | modality | files | bytes |
65
+ |---|---:|---:|
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+ | `cam_pose` | 110,000 | 163.3 MB |
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+ | `depth` | 110,000 | 16.9 GB |
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+ | `fisheye` | 220,000 | 63.4 GB |
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+ | `rgb` | 110,000 | 41.0 GB |
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+ | `masks` | 110,000 | 341.7 MB |
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+
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+ ## Quick start
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+
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+ ```bash
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+ pip install "rpx-benchmark[hub]"
76
+ hf auth login
77
+ ```
78
+
79
+ ```python
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+ from rpx_benchmark.dataset_hub import download_for_task
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+
82
+ # Pull just RGB + masks for the Easy difficulty tier — never the whole repo.
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+ res = download_for_task(task="segmentation", split="easy",
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+ repo_id="IRVLUTD/RPX")
85
+ print(res.local_dir, res.matched_scenes)
86
+ ```
87
+
88
+ ```bash
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+ # Or from the CLI:
90
+ python -m rpx_benchmark.dataset_hub.cli download \
91
+ --task segmentation --split easy \
92
+ --repo-id IRVLUTD/RPX
93
+ ```
94
+
95
+ A subsequent call for a different task on the same split (e.g.
96
+ `relative_pose`) reuses the cached RGB tars and only fetches the new
97
+ modality (`cam_pose`) as the delta.
98
+
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+ ## Repo layout
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+
101
+ ```
102
+ IRVLUTD/RPX/
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+ ├── manifest/
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+ │ ├── frames_v1.parquet # per-frame metadata
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+ │ ├── scene_name_mapping_v1.csv # original scene names to scene001..scene100
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+ │ ├── selected_sos_objects_v1.csv # selected 70-object SOS catalog
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+ │ ├── object_catalog_v1.json # SOS object/global-ID catalog
108
+ │ ├── mos_raw_mask_object_map_v1.csv # MOS local mask IDs from sam2 metadata
109
+ │ ├── mos_mask_object_map_v1.csv # MOS local mask IDs joined to SOS global IDs
110
+ │ ├── mos_mask_object_map_v1.parquet # parquet copy of the MOS map
111
+ │ └── current.json # default version per label modality
112
+ ├── splits/
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+ │ ├── scene_splits.json
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+ │ ├── easy.txt medium.txt hard.txt
115
+ ├── scenes/<scene_id>/<phase>/ # MOS
116
+ │ ├── rgb.tar depth.tar fisheye.tar
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+ │ └── labels/{cam_pose,masks,masks_aux,sam2_meta}/v1.tar
118
+ ├── objects/<object_id>/0/ # SOS
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+ │ └── (same modality structure)
120
+ ├── objects_meta/ # questionnaire dedup
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+ │ ├── _index.json
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+ │ └── <object_id>/questionnaire.json
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+ └── README.md ← this file
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+ ```
125
+
126
+ ## Object identity manifests
127
+
128
+ SOS objects use dataset-wide IDs from `manifest/selected_sos_objects_v1.csv`
129
+ and `manifest/object_catalog_v1.json`.
130
+
131
+ | field | meaning |
132
+ |---|---|
133
+ | `global_object_id` | New integer ID, 1 to 70 |
134
+ | `source_catalog_id` | Original catalog/PDF ID, kept as a string such as `88.2` |
135
+ | `object_id` | Actual folder name, such as `mug.2` |
136
+ | `questionnaire_path` | Linked questionnaire under `objects_meta/<object_id>/questionnaire.json` |
137
+
138
+ MOS masks use local IDs. A `local_mask_id` is only meaningful within one
139
+ `scene_id + phase`; it is not a global object ID. The raw source is
140
+ `scenes/<scene_id>/<phase_index>/labels/sam2_meta/v1.tar:sam2/mask_to_object.json`.
141
+
142
+ Use `manifest/mos_mask_object_map_v1.csv` or
143
+ `manifest/mos_mask_object_map_v1.parquet` to join MOS masks to SOS objects:
144
+
145
+ ```csv
146
+ scene_id,phase,local_mask_id,object_id,global_object_id,source_catalog_id,object_name
147
+ scene001,phase0,2,boot.2,11,18.2,boot
148
+ ```
149
+
150
+ That row means `scene001/0` mask ID `2` is `boot.2`, whose global object ID
151
+ is `11`, with questionnaire
152
+ `objects_meta/boot.2/questionnaire.json` and SOS template `objects/boot.2/`.
153
+
154
+ ## Tasks
155
+
156
+ ### Multi-object (use a difficulty split)
157
+
158
+ | recipe | inputs → labels |
159
+ |---|---|
160
+ | `monocular_depth` | ['rgb'] → ['depth'] |
161
+ | `rgbd_segmentation` | ['depth', 'rgb'] → ['masks'] |
162
+ | `segmentation` | ['rgb'] → ['masks'] |
163
+ | `relative_pose` | ['rgb'] → ['cam_pose'] |
164
+ | `rgbd_relative_pose` | ['depth', 'rgb'] → ['cam_pose'] |
165
+ | `stereo_depth` | ['fisheye'] → ['depth'] |
166
+ | `object_tracking` | ['rgb'] → ['masks'] |
167
+ | `vqa` | ['rgb'] → ['questionnaire', 'vqa'] |
168
+
169
+ ### Single-object (no split — these are object templates)
170
+
171
+ | recipe | inputs → labels |
172
+ |---|---|
173
+ | `object_templates` | ['rgb'] → ['masks'] |
174
+ | `object_templates_rgbd` | ['depth', 'rgb'] → ['masks'] |
175
+ | `object_pose_library` | ['depth', 'rgb'] → ['cam_pose', 'masks'] |
176
+
177
+ ## Label versioning
178
+
179
+ Labels live at `labels/<name>/v<N>.tar`. Newer versions land at new
180
+ paths; old versions stay reachable for reproducibility.
181
+
182
+ | modality | current version |
183
+ |---|---|
184
+ | `masks` | `v1` |
185
+ | `masks_aux` | `v1` |
186
+ | `sam2_meta` | `v1` |
187
+ | `cam_pose` | `v1` |
188
+
189
+ To pin to a specific version:
190
+
191
+ ```python
192
+ download_for_task(
193
+ task="relative_pose", split="easy", repo_id="itaykadosh/RPX",
194
+ label_versions={"cam_pose": "v1"}, # don't auto-upgrade to v2
195
+ )
196
+ ```
197
+
198
+ ## Citation
199
+
200
+ ```bibtex
201
+ @misc{rpx2026,
202
+ title = {RPX: Robot Perception X — A real-world RGB-D benchmark for
203
+ embodied perception},
204
+ author = {IRVL UT Dallas},
205
+ year = 2026,
206
+ url = {https://huggingface.co/datasets/itaykadosh/RPX},
207
+ }
208
+ ```
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+
210
+ ## License
211
+
212
+ Released under the **cc-by-4.0** license.
rpx_croissant.json ADDED
@@ -0,0 +1,274 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "@context": {
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+ "@language": "en",
4
+ "@vocab": "https://schema.org/",
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+ "citeAs": "cr:citeAs",
6
+ "column": "cr:column",
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+ "conformsTo": "dct:conformsTo",
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+ "cr": "http://mlcommons.org/croissant/",
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+ "data": {
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+ "@id": "cr:data",
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+ "@type": "@json"
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+ },
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+ "dataBiases": "cr:dataBiases",
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+ "dataCollection": "cr:dataCollection",
15
+ "dataType": {
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+ "@id": "cr:dataType",
17
+ "@type": "@vocab"
18
+ },
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+ "dct": "http://purl.org/dc/terms/",
20
+ "extract": "cr:extract",
21
+ "field": "cr:field",
22
+ "fileProperty": "cr:fileProperty",
23
+ "fileObject": "cr:fileObject",
24
+ "fileSet": "cr:fileSet",
25
+ "format": "cr:format",
26
+ "includes": "cr:includes",
27
+ "isLiveDataset": "cr:isLiveDataset",
28
+ "jsonPath": "cr:jsonPath",
29
+ "key": "cr:key",
30
+ "md5": "cr:md5",
31
+ "parentField": "cr:parentField",
32
+ "path": "cr:path",
33
+ "personalSensitiveInformation": "cr:personalSensitiveInformation",
34
+ "recordSet": "cr:recordSet",
35
+ "references": "cr:references",
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+ "regex": "cr:regex",
37
+ "repeated": "cr:repeated",
38
+ "replace": "cr:replace",
39
+ "sc": "https://schema.org/",
40
+ "separator": "cr:separator",
41
+ "source": "cr:source",
42
+ "subField": "cr:subField",
43
+ "transform": "cr:transform"
44
+ },
45
+ "@type": "sc:Dataset",
46
+ "name": "RPX",
47
+ "description": "RPX (Robot Perception X) is a unified real-world RGB-D benchmark for evaluating robot perception under embodied deployment conditions. It comprises 75,000 frames across 100 indoor scenes with ~70 daily object categories, captured under a three-phase reconfiguration protocol (clutter, human interaction, clean) using Intel D435 and T265 sensors. Ground-truth annotations include instance masks, tracklets, metric depth, 6-DoF camera pose, and language attributes, enabling evaluation of 8 perception tasks on identical scenes and objects.\n\nNote on on-disk shape: per-frame records are physically packed into per-(scene, phase, modality) tar shards on the HF repo (scenes/<scene>/<phase>/<modality>.tar for raw modalities; labels/<modality>/v<N>.tar for versioned labels). The rpx_benchmark.dataset_hub downloader resolves selective task+split downloads from these shards transparently.",
48
+ "conformsTo": "http://mlcommons.org/croissant/1.0",
49
+ "citeAs": "TODO: add bibtex citation after paper is published",
50
+ "license": "https://creativecommons.org/licenses/by/4.0/",
51
+ "url": "https://huggingface.co/datasets/IRVLUTD/RPX",
52
+ "version": "1.0.0",
53
+ "isLiveDataset": false,
54
+ "creator": {
55
+ "@type": "Organization",
56
+ "name": "Anonymous (for review)",
57
+ "url": "https://anonymous.4open.science/r/RPX"
58
+ },
59
+ "dataCollection": "Data was collected using a hand-held sensor rig (Intel RealSense D435 RGB-D + T265 tracking camera) by human operators in indoor environments. Each of 100 scenes was captured across three phases: clutter, human interaction, and clean. Per-frame instance masks were generated using SAM2 with iterative human refinement. Camera poses are from T265 visual-inertial odometry. Language annotations are derived from FewSOL object questionnaires.",
60
+ "dataBiases": "The dataset is limited to indoor tabletop and room-scale environments with D435 depth range (0.3-3m). Object categories skew toward household and laboratory manipulation objects. Capture trajectories are human-driven and may not cover all robot motion profiles. All capture was performed by lab personnel; annotator diversity is limited.",
61
+ "personalSensitiveInformation": "The dataset contains video footage of human hands during the interaction phase. No faces, biometric identifiers, or personally identifiable information are captured or released. Participants provided informed consent for data collection and release.",
62
+ "distribution": [
63
+ {
64
+ "@type": "cr:FileObject",
65
+ "@id": "rpx-sample",
66
+ "name": "rpx-sample",
67
+ "description": "5-scene reviewer sample (<4GB), available without registration",
68
+ "contentUrl": "https://anonymous.4open.science/r/RPX/sample",
69
+ "encodingFormat": "application/zip",
70
+ "sha256": "TODO"
71
+ },
72
+ {
73
+ "@type": "cr:FileObject",
74
+ "@id": "rpx-full",
75
+ "name": "rpx-full",
76
+ "description": "Full 75,000-frame dataset with all annotations",
77
+ "contentUrl": "https://anonymous.4open.science/r/RPX/full",
78
+ "encodingFormat": "application/zip",
79
+ "sha256": "TODO"
80
+ },
81
+ {
82
+ "@type": "cr:FileObject",
83
+ "@id": "rpx-manifests",
84
+ "name": "rpx-manifests",
85
+ "description": "JSON manifest files for train/val/test splits and ESD labels",
86
+ "contentUrl": "https://anonymous.4open.science/r/RPX/manifests",
87
+ "encodingFormat": "application/json"
88
+ }
89
+ ],
90
+ "recordSet": [
91
+ {
92
+ "@type": "cr:RecordSet",
93
+ "@id": "frames",
94
+ "name": "frames",
95
+ "description": "Per-frame records with RGB, depth, instance mask, camera pose, phase, and ESD label",
96
+ "field": [
97
+ {
98
+ "@type": "cr:Field",
99
+ "@id": "frames/id",
100
+ "name": "id",
101
+ "description": "Unique frame identifier (scene_phase_framenum)",
102
+ "dataType": "sc:Text"
103
+ },
104
+ {
105
+ "@type": "cr:Field",
106
+ "@id": "frames/scene_id",
107
+ "name": "scene_id",
108
+ "description": "Scene index (0-99)",
109
+ "dataType": "sc:Integer"
110
+ },
111
+ {
112
+ "@type": "cr:Field",
113
+ "@id": "frames/phase",
114
+ "name": "phase",
115
+ "description": "Scene phase: clutter | interaction | clean",
116
+ "dataType": "sc:Text"
117
+ },
118
+ {
119
+ "@type": "cr:Field",
120
+ "@id": "frames/difficulty",
121
+ "name": "difficulty",
122
+ "description": "ESD difficulty level: easy | medium | hard",
123
+ "dataType": "sc:Text"
124
+ },
125
+ {
126
+ "@type": "cr:Field",
127
+ "@id": "frames/rgb_path",
128
+ "name": "rgb_path",
129
+ "description": "Relative path to RGB image (PNG, 1920x1080)",
130
+ "dataType": "sc:Text"
131
+ },
132
+ {
133
+ "@type": "cr:Field",
134
+ "@id": "frames/depth_path",
135
+ "name": "depth_path",
136
+ "description": "Relative path to depth map (NPY float32, metres)",
137
+ "dataType": "sc:Text"
138
+ },
139
+ {
140
+ "@type": "cr:Field",
141
+ "@id": "frames/mask_path",
142
+ "name": "mask_path",
143
+ "description": "Relative path to instance mask (PNG, pixel values = instance IDs)",
144
+ "dataType": "sc:Text"
145
+ },
146
+ {
147
+ "@type": "cr:Field",
148
+ "@id": "frames/camera_pose",
149
+ "name": "camera_pose",
150
+ "description": "4x4 SE(3) camera-to-world matrix from T265 (float64)",
151
+ "dataType": "sc:Text"
152
+ },
153
+ {
154
+ "@type": "cr:Field",
155
+ "@id": "frames/split",
156
+ "name": "split",
157
+ "description": "Dataset split: train | val | test",
158
+ "dataType": "sc:Text"
159
+ }
160
+ ]
161
+ },
162
+ {
163
+ "@type": "cr:RecordSet",
164
+ "@id": "tracklets",
165
+ "name": "tracklets",
166
+ "description": "Per-object tracklets with consistent IDs across frames within a phase",
167
+ "field": [
168
+ {
169
+ "@type": "cr:Field",
170
+ "@id": "tracklets/scene_id",
171
+ "name": "scene_id",
172
+ "dataType": "sc:Integer"
173
+ },
174
+ {
175
+ "@type": "cr:Field",
176
+ "@id": "tracklets/phase",
177
+ "name": "phase",
178
+ "dataType": "sc:Text"
179
+ },
180
+ {
181
+ "@type": "cr:Field",
182
+ "@id": "tracklets/track_id",
183
+ "name": "track_id",
184
+ "description": "Unique object track identifier within scene+phase",
185
+ "dataType": "sc:Text"
186
+ },
187
+ {
188
+ "@type": "cr:Field",
189
+ "@id": "tracklets/object_category",
190
+ "name": "object_category",
191
+ "description": "Object category name",
192
+ "dataType": "sc:Text"
193
+ },
194
+ {
195
+ "@type": "cr:Field",
196
+ "@id": "tracklets/language_description",
197
+ "name": "language_description",
198
+ "description": "Natural language referring expression from FewSOL attributes",
199
+ "dataType": "sc:Text"
200
+ }
201
+ ]
202
+ },
203
+ {
204
+ "@type": "cr:RecordSet",
205
+ "@id": "esd_scores",
206
+ "name": "esd_scores",
207
+ "description": "Per-(scene, phase) ESD feature values and RPX-DS scores",
208
+ "field": [
209
+ {
210
+ "@type": "cr:Field",
211
+ "@id": "esd/scene_id",
212
+ "name": "scene_id",
213
+ "dataType": "sc:Integer"
214
+ },
215
+ {
216
+ "@type": "cr:Field",
217
+ "@id": "esd/phase",
218
+ "name": "phase",
219
+ "dataType": "sc:Text"
220
+ },
221
+ {
222
+ "@type": "cr:Field",
223
+ "@id": "esd/rpx_ds",
224
+ "name": "rpx_ds",
225
+ "description": "RPX Difficulty Score (higher = harder)",
226
+ "dataType": "sc:Float"
227
+ },
228
+ {
229
+ "@type": "cr:Field",
230
+ "@id": "esd/difficulty",
231
+ "name": "difficulty",
232
+ "description": "ESD split assignment: easy | medium | hard",
233
+ "dataType": "sc:Text"
234
+ },
235
+ {
236
+ "@type": "cr:Field",
237
+ "@id": "esd/occ_mean",
238
+ "name": "occ_mean",
239
+ "dataType": "sc:Float"
240
+ },
241
+ {
242
+ "@type": "cr:Field",
243
+ "@id": "esd/iter_mean",
244
+ "name": "iter_mean",
245
+ "dataType": "sc:Float"
246
+ },
247
+ {
248
+ "@type": "cr:Field",
249
+ "@id": "esd/area_cv",
250
+ "name": "area_cv",
251
+ "dataType": "sc:Float"
252
+ },
253
+ {
254
+ "@type": "cr:Field",
255
+ "@id": "esd/depth_invalid_mask",
256
+ "name": "depth_invalid_mask",
257
+ "dataType": "sc:Float"
258
+ },
259
+ {
260
+ "@type": "cr:Field",
261
+ "@id": "esd/vis_instability",
262
+ "name": "vis_instability",
263
+ "dataType": "sc:Float"
264
+ },
265
+ {
266
+ "@type": "cr:Field",
267
+ "@id": "esd/jerk",
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