| --- |
| pretty_name: 3DSAV (3D Scene Assertion Verification) |
| language: |
| - en |
| task_categories: |
| - question-answering |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - 3d-scene-understanding |
| - assertion-verification |
| - visual-reasoning |
| - scannet |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.jsonl |
| - split: validation |
| path: validation.jsonl |
| - split: test |
| path: test.jsonl |
| --- |
| |
| # 3DSAV: 3D Scene Assertion Verification |
|
|
| 3DSAV is a benchmark for verifying natural-language assertions against 3D indoor scenes. Given a scene and an assertion, a model predicts a binary answer, `yes` or `no`. The dataset contains **22,520 annotations across 682 ScanNet scenes**, covering six semantic types. |
|
|
| This repository hosts the assertion annotations and their original scene-disjoint splits. ScanNet geometry, RGB frames, and model predictions are not included. |
|
|
| ## Load the dataset |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("csdjade/3DSAV") |
| print(dataset) |
| print(dataset["train"][0]) |
| ``` |
|
|
| The `validation` split corresponds to `3dsav_val.json` in the original release. |
|
|
| ## Splits |
|
|
| | Split | Annotations | ScanNet scenes | yes | no | |
| |---|---:|---:|---:|---:| |
| | train | 18,620 | 547 | 9,005 | 9,615 | |
| | validation | 2,001 | 68 | 924 | 1,077 | |
| | test | 1,899 | 67 | 875 | 1,024 | |
| | Total | 22,520 | 682 | 10,804 | 11,716 | |
|
|
| Question IDs and scene IDs are mutually disjoint across the three splits. The test split contains the original 1,899 examples. |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `question_id` | string | Unique annotation identifier. | |
| | `scene_id` | string | ScanNet scene identifier, such as `scene0667_00`. | |
| | `q` | string | Natural-language assertion or binary verification question. | |
| | `a` | string | Ground-truth answer: `yes` or `no`. | |
| | `name` | list of strings | Object names supplied with the annotation. | |
| | `ids` | list of integers | Corresponding object IDs in the source scene annotation. | |
| | `type` | string | One of the six semantic types below. | |
|
|
| Object IDs are scene-local. The `name` and `ids` lists have matching lengths. |
|
|
| ```json |
| { |
| "question_id": "scene0667_00-23", |
| "scene_id": "scene0667_00", |
| "q": "Is there a stool under the table?", |
| "a": "yes", |
| "name": ["table", "stool"], |
| "ids": [5, 43], |
| "type": "spatial" |
| } |
| ``` |
|
|
| ## Semantic types |
|
|
| | Type | Description | Total | |
| |---|---|---:| |
| | `spatial` | Spatial relationships between scene elements. | 5,532 | |
| | `negation` | Negative logic and its scope. | 4,421 | |
| | `attribute` | Observable visual properties of objects. | 4,171 | |
| | `quantity` | Counting and quantification. | 3,746 | |
| | `existence` | Whether an object is present. | 2,576 | |
| | `verification` | Comprehensive assertions outside the other five categories. | 2,074 | |
|
|
| The benchmark was constructed through scene-aware language-model generation followed by human verification. |
|
|
| ## Release format |
|
|
| The three JSONL files preserve the original fields, records, labels, and row order. The `type` field was added to each split by an exact, unique join on `question_id` against `3dsav_all.json`. Every original field was checked against that full annotation file. |
|
|
| [`3DSAV_original_annotations.zip`](3DSAV_original_annotations.zip) contains the four original JSON files with their contents unchanged: `3dsav_all.json`, `3dsav_train.json`, `3dsav_val.json`, and `3dsav_test.json`. |
|
|
| ## Scene data and usage |
|
|
| Obtain the underlying scenes through the [ScanNet project](http://www.scan-net.org/) and follow its access conditions and terms. Use `scene_id` to connect an assertion to its scene. Evaluating the benchmark requires the corresponding scene data; the text annotations alone do not provide the visual evidence. |
|
|
| The source annotation package does not specify a separate annotation license. No additional license is asserted by this upload. ScanNet's terms continue to apply to the underlying scene data. |
|
|
| ## Reference |
|
|
| **3D Scene Assertion Verification**. Jun Lin, Jiayu Ding, Xiangtian Si, Xitong Cao, Lixin Hong, Zhang Chen, Chenxi Lv, and Wenqian Wang, 2026. |
|
|
