ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models

Dataset Description

We introduce ViewSpatial-Bench, a comprehensive benchmark with over 5,700 question-answer pairs across 1,000+ 3D scenes from ScanNet and MS-COCO validation sets. This benchmark evaluates VLMs' spatial localization capabilities from multiple perspectives, specifically testing both egocentric (camera) and allocentric (human subject) viewpoints across five distinct task types.

ViewSpatial-Bench addresses a critical gap: while VLMs excel at spatial reasoning from their own perspective, they struggle with perspective-taking—adopting another entity's spatial frame of reference—which is essential for embodied interaction and multi-agent collaboration.The figure below shows the construction pipeline and example demonstrations of our benchmark.

The dataset contains the following fields:

Field Name	Description
question_type	Type of spatial reasoning task, includes 5 distinct categories for evaluating different spatial capabilities
image_path	Path to the source image, includes data from two sources: scannetv2_val (ScanNet validation set) and val2017 (MS-COCO validation set)
question	The spatial reasoning question posed to the model
answer	The correct answer to the question
choices	Multiple choice options available for the question

• Language(s) (NLP): en
• License: apache-2.0

Uses

I. With HuggingFace datasets library.

from datasets import load_dataset
ds = load_dataset("lidingm/ViewSpatial-Bench")

II. Evaluation using Open-Source Code.

Evaluate using our open-source evaluation code available on Github.(Coming Soon)

# Clone the repository
git clone https://github.com/lidingm/ViewSpatial-Bench.git
cd ViewSpatial-Bench

# Install dependencies
pip install -r requirements.txt

# Run evaluation
python evaluate.py --model_path your_model_path

You can configure the appropriate model parameters and evaluation settings according to the framework's requirements to obtain performance evaluation results on the ViewSpatial-Bench dataset.

Benchamrk

We provide benchmark results for various open-source models as well as GPT-4o and Gemini 2.0 Flash on our benchmark. More model evaluations will be added.

Model	Camera-based Tasks			Person-based Tasks				Overall
	Rel. Dir.	Obj. Ori.	Avg.	Obj. Ori.	Rel. Dir.	Sce. Sim.	Avg.
InternVL2.5 (2B)	38.52	22.59	32.79	47.09	40.02	25.70	37.04	34.98
Qwen2.5-VL (3B)	43.43	33.33	39.80	39.16	28.62	28.51	32.14	35.85
Qwen2.5-VL (7B)	46.64	29.72	40.56	37.05	35.04	28.78	33.37	36.85
LLaVA-NeXT-Video (7B)	26.34	19.28	23.80	44.68	38.60	29.05	37.07	30.64
LLaVA-OneVision (7B)	29.84	26.10	28.49	22.39	31.00	26.88	26.54	27.49
InternVL2.5 (8B)	49.41	41.27	46.48	46.79	42.04	32.85	40.20	43.24
Llama-3.2-Vision (11B)	25.27	20.98	23.73	51.20	32.19	18.82	33.61	28.82
InternVL3 (14B)	54.65	33.63	47.09	33.43	37.05	31.86	33.88	40.28
Kimi-VL-Instruct (16B)	26.85	22.09	25.14	63.05	43.94	20.27	41.52	33.58
GPT-4o	41.46	19.58	33.57	42.97	40.86	26.79	36.29	34.98
Gemini 2.0 Flash	45.29	12.95	33.66	41.16	32.78	21.90	31.53	32.56
Random Baseline	25.16	26.10	25.50	24.60	31.12	26.33	27.12	26.33

Citation

Coming Soon