README.md

---
license: other
license_name: mindview-source-dependent-terms
license_link: https://huggingface.co/datasets/wangyanbo/MindView-Bench/blob/main/DATA_LICENSE.md
language:
- en
tags:
- mindview
- benchmark
- multi-view
- spatial-reasoning
- image-generation
size_categories:
- 1K<n<10K
configs:
- config_name: benchmark
  default: true
  data_files:
  - split: test
    path: data/test.jsonl
- config_name: context_scaling
  data_files:
  - split: test
    path: context_scaling/metadata.jsonl
---

# MindView-Bench

**MindView benchmarks language-guided target-view generation from multiple observations of an indoor scene.** Models combine reference RGB images with a spatial instruction to generate the requested viewpoint while preserving scene geometry and appearance.

The official test set contains **1,500 examples from 513 scenes** across ScanNet, ScanNet++, and ARKitScenes. Separate context-scaling experiments reuse these same examples.

[Code](https://github.com/Yanbo-23/MindView) · [Training data](https://huggingface.co/datasets/wangyanbo/MindView-Train) · [BAGEL-SFT weights](https://huggingface.co/wangyanbo/MindView-BAGEL-SFT) · [Collection](https://huggingface.co/collections/wangyanbo/mindview-6acf6b7a4a2e25efd51989f5)

## Contents

| Path | Purpose |
|---|---|
| `data/test.jsonl` | Official 1,500-example test index: ordered generator inputs and instructions |
| [`context_scaling/`](context_scaling/README.md) | Additional C3/C6/C10 experiments: 4,500 aligned configuration records |
| `evaluation/references.jsonl` | Fixed scoring references, held-out targets, depth, and source paths |
| `evaluation/protocol.json` | Tasks, metric definitions, and aggregation rules |
| `assets/{rgb,depth}/*.tar` | Original image bytes in 20 ordinary TAR archives |
| `manifests/` | Per-asset and per-archive paths, sizes, and checksums |
| `scripts/prepare_data.py` | Verify the download and safely extract assets |
| `RELEASE.json`, `SHA256SUMS` | Release inventory and file checksums |

The archives contain **14,276 RGB images and 14,276 depth maps**, deduplicated across test inputs, scoring references, targets, and additional experiments. Archive counts do not indicate images per example.

## Generator inputs and scoring

**Official generation uses six ordered context RGB images and the instruction in each test record.** VGGT scoring uses a fixed set of **ten reference RGB images**, plus the held-out target and generated image: twelve RGB images in total. Sensor depth supports metric calibration. The extra scoring references, target RGB, and depth must not enter the generator beyond its prescribed inputs.

Seven tasks cover translation, rotation, composition, relational object grounding, target-facing reorientation, object-centric repositioning, and multi-cue viewpoint inference. Six primary metrics—DINO-I, Semantic, Spatial, Quality, VGGT-RScore, and VGGT-TScore—are averaged within each task, then equally across tasks. **Overall** averages the six resulting scores; auxiliary CLIP-I is excluded. Report context-scaling results separately.

The frozen subset was selected using model-level diagnostics; results for methods involved in that selection are selection-conditioned.

## Download and prepare

The first complete release is tagged `v1.0`. Use the same revision for annotations and assets. Install the Hugging Face CLI if needed:

```bash
pip install -U huggingface_hub
hf download wangyanbo/MindView-Bench \
  --repo-type dataset --revision "v1.0" \
  --local-dir ./MindView-Bench
```

With Python 3.10 or later, verify the complete snapshot, then extract into a separate directory:

```bash
python MindView-Bench/scripts/prepare_data.py \
  --repo MindView-Bench --verify-only
python MindView-Bench/scripts/prepare_data.py \
  --repo MindView-Bench --output ./mindview-data
```

Extraction restores `mindview-data/scannet/`, `scannetpp/`, and `arkitscenes/`. Matching existing files are reused; conflicting files and unsafe archive entries are rejected. Images and depth values are preserved. Depth is stored as unsigned 16-bit PNG in millimeters; evaluation converts valid values to meters.

For annotations only:

```python
from datasets import load_dataset

test = load_dataset(
    "wangyanbo/MindView-Bench", "benchmark",
    split="test", revision="v1.0",
)
assert len(test) == 1500
```

Annotations contain frame IDs and paths; TAR assets are downloaded and extracted separately rather than streamed as decoded `Image` features.

## Runtime configuration

In the project runtime, explicitly set `INPUT_JSON` to `MindView-Bench/data/test.jsonl` and the three dataset roots to the extracted source directories. For VGGT, set `VGGT_CANONICAL_JSON` to `MindView-Bench/context_scaling/metadata.jsonl`; the scorer selects its ten-view records. `evaluation/references.jsonl` documents the scoring assets but is not automatically consumed by the current scorer.

Older launchers default to the combined 4,500-record index. Do not use that default for official generation. Package integrity has been checked; GPU evaluation with this release layout still requires validation.

## Terms and contact

Source-data terms remain applicable; this package does not relicense ScanNet, ScanNet++, or ARKitScenes. Read [DATA_LICENSE.md](DATA_LICENSE.md) before use.

Contact: Yihao Liu (`liuyihao@pjlab.org.cn`) and Jiwen Lu (`lujiwen@tsinghua.edu.cn`). Include the revision and sample ID when reporting issues.