Datasets:

suijinru
/

MultiViewBench

pretty_name: MultiView-Bench
license: mit
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - multi-view
  - spatial-reasoning
  - vlm-benchmark
  - synthetic
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    drop_labels: true

MultiView-Bench

Evaluation data for MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs (arXiv:2607.08970).

This repository currently contains the synthetic image-and-text portion of the benchmark. The real-world subset is not included; see the scope note below.

2,400 evaluation samples across 24 benchmark variants
7,900 PNG views (2.94 GiB logical image data)
One, three, or six images per sample
English question text and deterministic coordinate answer
Single-agent and multi-agent question schemas

The 3D Real World / exp3D_real_world_50 subset is intentionally excluded. It uses 3DCoMPaT++ source assets and is not part of this release. This package also contains no GLTF, BIN, model asset, source code, credentials, or model outputs.

Task previews

Each sample asks for a relative 3D position and must be solved by integrating the evidence across its views.

Colored-object three-view task

Question: What is the relative position of the olive cone to the indigo cube?
Answer: (-X, -Y, -Z)

Front	Side	Top

3D isometric task

Question: What is the relative position of the cylinder to the cone?
Answer: (0X, 0Y, -Z)
The full sample contains six views; three are shown here.

View 0	View 2	View 4

Full 3D reasoning (DoF=3)

Task variant: 3D DoF=3 (unconstrained placement along X, Y, and Z)
Question: What is the relative position of the cube to the cylinder?
Answer: (-X, +Y, +Z)
The full sample contains six views; three are shown here.

View 0	View 2	View 4

Single-view 3D task

Task variant: 3D Single View
Question: What is the relative position of the cube to the cylinder?
Answer: (-X, +Y, +Z)
Unlike the multi-view variants, this sample exposes only one isometric view.

Grid-assisted three-view task

Task variant: 2D Three Views Grids
Question: What is the relative position of the cylinder to the sphere?
Answer: (0X, -Y, -Z)
The projected coordinate grids provide additional alignment cues in each view.

Front	Side	Top

Rotated-coordinate task (90°)

Task variant: 2D Three Views Rotation (90°)
Question: What is the relative position of the cube to the cone?
Answer: (+X, 0Y, -Z)
The coordinate frame is rotated to test whether the model follows the visible axes rather than relying on a conventional orientation.

Front	Side	Top

Multi-agent three-view task

Task variant: 2D Three Views multiagent (Rotation 0)
Question: What is the relative position of the sphere to the cone?
Answer: (-X, -Y, +Z)
Each view has its own axis-pair prompt (front_question, side_question, or top_question) so three view-specific responses can be combined into the full XYZ answer.

Front agent: XZ	Side agent: YZ	Top agent: XY

Load

After upload:

from datasets import load_dataset

dataset = load_dataset("suijinru/MultiViewBench", split="test")
sample = dataset[0]
print(sample["benchmark"], sample["question"], sample["answer"])
print(sample["images"])

For a local pre-upload check:

dataset = load_dataset("imagefolder", data_dir="test", split="train")

ImageFolder derives the images column from the images_file_names list in test/metadata.jsonl. The Hub recognizes columns ending in _file_names as image lists.

Fields

benchmark: benchmark variant identifier
task_variant: human-readable task variant label used to group corresponding results in the paper; it is not a row number. For the 3D variants, DoF describes the object-placement constraint: DoF=1 restricts objects to the same axis, DoF=2 restricts them to the same plane, and DoF=3 allows unconstrained placement in 3D space.
task_id: task identifier within the variant
mode: single or multi
question: complete prompt for single-agent samples
front_question, side_question, top_question: per-view prompts for multi-agent samples
answer: expected coordinate answer
images_file_names: ordered relative paths to all views
num_views: number of views

All rows are evaluation data; do not use them for training when reporting MultiView-Bench scores.