---
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.