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README.md

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+## Spatial Region 3D (SR-3D) Aware Benchmark
+
+
+![sr3d-bench](https://cdn-uploads.huggingface.co/production/uploads/63d07b75f2341424c80a5de8/Thv5JKZ6c95upKXrjM10K.png)
+
+**Paper:** https://arxiv.org/abs/2509.13317  
+**Project page:** https://www.anjiecheng.me/sr3d  
+**Code:** https://github.com/AnjieCheng/SR-3D  
+
+
+> [!IMPORTANT]
+> ***[Feb. 18, 2026] UPDATE:** To improve compatibility with general-purpose VLMs, the benchmark is reformulated into multiple-choice and numerical questions following the VSI-Bench evaluation protocol.  Videos are annotated with set-of-marks to explicitly indicate regions.  The benchmark will be compatible with VLMEvalKit and lmms-eval.*
+
+### Overview
+
+SR-3D-Bench is a region-level spatial understanding benchmark for videos introduced in the SR-3D paper.
+
+Existing video benchmarks lack explicit region annotations, making spatial reasoning ambiguous when multiple similar objects are present or when referring to specific areas in a scene. SR-3D-Bench addresses this limitation by providing region-grounded spatial question answering.
+
+The benchmark is curated from ScanNet, ARKitScenes, and Matterport3D video scan datasets with 3D ground truth. It uses oriented 3D bounding boxes from EmbodiedScan, where objects are aligned in a canonical coordinate system to ensure accurate width, height, and distance measurements.
+
+SR-3D-Bench includes:
+- Qualitative QA: choice-based, predicate-based, and multiple-choice questions  
+- Quantitative QA: object width, height, and spatial distance estimation  
+
+All QA pairs are generated using template-based conversation generation.
+
+### Usage
+
+```python
+from datasets import load_dataset
+
+sr3d_bench = load_dataset("a8cheng/SR-3D-Bench")
+```
+