Datasets:
Hongxing Li commited on
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README.md
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size_categories:
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<a href="" target="_blank">
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-SpatialLadder-red?logo=arxiv" height="20" />
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</a>
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<a href="" target="_blank">
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<img alt="Website" src="https://img.shields.io/badge/🌎_Website-SpaitalLadder-blue.svg" height="20" />
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</a>
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<a href="https://github.com/ZJU-REAL/SpatialLadder" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/Code-SpaitalLadder-white?logo=github" height="20" />
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</a>
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<a href="" target="_blank">
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<img alt="Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Model-SpatialLadder--3B-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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<a href="" target="_blank">
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<img alt="Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Data-SpatialLadder--26k-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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</div>
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# Spatial Perception and Reasoning Benchmark (SPBench)
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This repository contains the Spatial Perception and Reasoning Benchmark (SPBench), introduced in [SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models]().
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## Files
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## Dataset Description
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SPBench is a comprehensive evaluation suite designed to assess the spatial perception and reasoning capabilities of Vision-Language Models (VLMs). SPBench consists of two complementary benchmarks, SPBench-SI and SPBench-MV, corresponding to single-image and multi-view modalities, respectively. Both benchmarks are constructed using the standardized pipeline applied to the ScanNet validation set, ensuring systematic coverage across diverse spatial reasoning tasks.
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SPBench-SI serves as a single-image evaluation benchmark that measures models’ ability to perform spatial understanding and reasoning from individual viewpoints. It encompasses four task categories—absolute distance, object size, relative distance, and relative direction—containing a total of 1,009 samples. In contrast, SPBench-MV focuses on multi-view spatial reasoning, requiring models to jointly model spatial relationships across multiple viewpoints. It further includes object counting tasks to evaluate models’ capability in identifying and enumerating objects within multi-view scenarios. Both benchmarks undergo rigorous quality control through a combination of standardized pipeline filtering strategies and manual curation, ensuring data disambiguation and high-quality annotations suitable for reliable evaluation.
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