Datasets:

RainChow
/

PinpointQA

@@ -1,311 +1,313 @@
----
-pretty_name: PinpointQA
-language:
-- en
-license: apache-2.0
-size_categories:
-- 10K<n<100K
-task_categories:
-- video-text-to-text
-tags:
-- benchmark
-- spatial-understanding
-- small-object
-- indoor-scenes
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: train.jsonl
-  - split: validation
-    path: validation.jsonl
-  - split: test
-    path: test.jsonl
----
-# PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos
-> **Important:** This repository releases **benchmark annotations** and **grounded intermediate spatial representations** only. It does **not** redistribute the original scene assets or converted video files.
-## 🧭 Overview
-PinpointQA focuses on a practical question: given a known small object such as a phone, charger, remote, or bottle, can a model determine whether it appears, localize it through nearby references, describe its position precisely, and provide an output that is directly useful for downstream systems?
-In addition to benchmark annotations, this repository also releases grounded **intermediate spatial representations** constructed during scene curation. These files preserve the target-centered local spatial context used to generate the released QA pairs and can support further analysis or the construction of additional grounded tasks.
-## 👀 Task Overview
-PinpointQA is organized as a progressive four-stage benchmark:
-| Task | Name | Goal | Output Format |
-|---|---|---|---|
-| TPV | Target Presence Verification | Determine whether a queried small object appears in the scene | `Yes` / `No` |
-| NRI | Nearest Reference Identification | Identify the nearest reference object to the target, excluding the support surface | Multiple choice |
-| FSD | Fine-Grained Spatial Description | Describe the target location with support surface, nearby references, and centimeter-level distances | Natural language |
-| SSP | Structured Spatial Prediction | Output the same grounded spatial information in structured form | JSON |
-## 📊 Key Statistics
-- **Scenes:** 1,024
-- **QA pairs:** 10,094
-- **Canonical target categories:** 102
-- **Source datasets:** ScanNet++, ScanNet200
-- **Task distribution over all released QA pairs:** TPV 26.47%, NRI 23.10%, FSD 25.08%, SSP 25.34%
-- **Source distribution over all released QA pairs:** ScanNet++ 73.2%, ScanNet200 26.8%
-- **Released splits:** train 6,121 / validation 1,954 / test 2,019
-## 🏷️ Category Naming Note
-PinpointQA contains **102 canonical target categories** at the benchmark-definition level.
-You may notice that the dataset viewer reports **more distinct string values** in the target column. This is expected: some semantically equivalent or near-equivalent names are preserved as **surface forms** in released text fields for readability and compatibility with source annotations or task phrasing. Examples include naming variants such as **`mobile phone`** and **`phone`**.
-When reporting benchmark statistics in the paper and project page, we count categories at the **canonical category** level rather than the raw string-surface level.
-## 🚀 Quick Start
-### Install dependencies
-```bash
-pip install datasets
-```
-### Load the dataset
-```python
-from datasets import load_dataset
-dataset = load_dataset("RainChow/PinpointQA")
-print(dataset)
-print(dataset["train"][0])
-```
-### Access a specific split
-```python
-train_set = dataset["train"]
-val_set = dataset["validation"]
-test_set = dataset["test"]
-```
-### Save the dataset locally
-```python
-from datasets import load_dataset
-dataset = load_dataset("RainChow/PinpointQA")
-dataset.save_to_disk("./PinpointQA_hf")
-```
-## 🗂️ Dataset Organization
-```text
-PinpointQA/
-├── train.jsonl
-├── validation.jsonl
-├── test.jsonl
-├── intermediate_spatial_representations/
-│   ├── scene_xxx.json
-│   ├── scene_yyy.json
-│   └── ...
-└── README.md
-```
-### Released Fields
-- `id`: globally unique sample identifier
-- `scene_id`: scene identifier
-- `source_dataset`: `scannetpp` or `scannet200`
-- `local_sample_id`: scene-local sample index
-- `task`: short task label (`TPV`, `NRI`, `FSD`, `SSP`)
-- `question_type`: original long-form task name
-- `instruction`: task instruction
-- `question`: user-facing question text
-- `choices`: candidate options for NRI, otherwise `null`
-- `answer`: ground-truth answer
-- `target`: queried small object name used in the released sample text
-- `split`: split name
-### Example Record
-```json
-{
-  "id": "scene0000_00_0",
-  "scene_id": "scene0000_00",
-  "source_dataset": "scannet200",
-  "local_sample_id": "0",
-  "task": "TPV",
-  "question_type": "target presence verification",
-  "instruction": "Answer only with exactly one word: Yes or No. Do not add any explanation.",
-  "question": "In the entire scene, did the coffee kettle appear?",
-  "choices": null,
-  "answer": "No",
-  "target": "coffee kettle",
-  "split": "train"
-}
-```
-### Field Notes by Task
-- **TPV:** `answer` is `Yes` or `No`
-- **NRI:** `choices` contains four candidate objects; `answer` is the correct option text
-- **FSD:** `answer` is a natural-language location description
-- **SSP:** `answer` is a JSON-formatted string representing structured spatial grounding
-### Intermediate Spatial Representations
-The `intermediate_spatial_representations/` folder stores the grounded scene-level representations used to instantiate TPV, NRI, FSD, and SSP.
-- Each file corresponds to a scene and is aligned with `scene_id`.
-- These files preserve the target-centered local spatial context used for QA construction.
-- The released content includes grounded information such as target objects, support surfaces, nearby references, and local spatial relations/distances.
-For example, a file such as `scene0000_00.json` corresponds to `scene_id = "scene0000_00"` and provides the grounded scene context from which the released QA samples for that scene are derived.
-## 📐 Spatial Semantics
-### Support Surface vs. Reference Objects
-The **support surface** is the surface that directly supports the target object in the final grounded representation.
-- In **NRI**, the support surface is **excluded** from candidate reference options.
-- In **FSD** and **SSP**, the support surface is retained as a distinct field because it is often a necessary localization anchor.
-- Nearby **references** are additional local objects used to describe or structure the final location of the target.
-Depending on scene semantics and released wording, a surface-like object may appear in text fields as a location anchor, but the benchmark definition still treats **support surface** and **reference objects** as functionally different roles.
-### Distances
-Distances in FSD and SSP are derived from grounded scene geometry and expressed in **centimeters** in the released benchmark outputs.
-## 🧱 Source Data Preparation
-This repository releases **benchmark annotations** and **intermediate spatial representations** only. It does **not** redistribute the original scene assets or converted videos.
-To reproduce video-based experiments, users should first obtain the original assets from the official sources of **ScanNet++** and **ScanNet v2 / ScanNet200**, subject to their respective licenses and access requirements. Note that **ScanNet200** shares the same underlying source data as **ScanNet v2** and mainly differs in annotation parsing and label space, so the video assets used here still come from the **ScanNet v2** RGB-D data.
-### ScanNet++
-- Official website: [ScanNet++](https://scannetpp.mlsg.cit.tum.de/scannetpp/)
-- Obtain access through the official ScanNet++ release.
-- Download the scenes required by your target split or evaluation subset.
-- Match local assets to the released `scene_id` values.
-### ScanNet v2 / ScanNet200
-- Official ScanNet website: [ScanNet](http://www.scan-net.org/)
-- ScanNet200 benchmark documentation: [ScanNet200 Benchmark Documentation](https://kaldir.vc.in.tum.de/scannet_benchmark/documentation)
-- Obtain access to the original data and prepare the scenes required by your pipeline.
-- Match local assets to the released `scene_id` values used in this benchmark.
-### Video Conversion Tools
-The source assets from **ScanNet++** and **ScanNet v2 / ScanNet200** are **not distributed as ready-to-use MP4 videos**. If your pipeline expects standard video files, we provide conversion scripts in the project GitHub repository:
-- `tools/convert_mkv_to_mp4.py`
-- `tools/convert_sens_to_mp4.py`
-Tools folder:
-- [https://github.com/rainchowz/PinpointQA/tree/main/tools](https://github.com/rainchowz/PinpointQA/tree/main/tools)
-### Recommended Local Organization
-```text
-workspace/
-├── PinpointQA/
-│   ├── train.jsonl
-│   ├── validation.jsonl
-│   ├── test.jsonl
-│   └── intermediate_spatial_representations/
-├── raw_data/
-│   ├── scannetpp/
-│   └── scannet200/
-└── videos/
-    ├── scene_or_video_1.mp4
-    ├── scene_or_video_2.mp4
-    └── ...
-```
-Users may organize local files differently depending on their own training or inference pipeline.
-## 🧠 Intended Use
-PinpointQA is intended for:
-- benchmarking multimodal models on small object-centric spatial understanding in indoor videos
-- instruction tuning or supervised fine-tuning for grounded spatial QA tasks
-- studying progressive capability breakdown from target presence to structured spatial output
-- analyzing reference-based localization and spatial grounding behavior in multimodal systems
-## 🚫 Out-of-Scope Use
-PinpointQA is **not** intended as:
-- a general-purpose benchmark for all video understanding abilities
-- a substitute for open-world object tracking or dense video captioning benchmarks
-- a benchmark for outdoor scenes, unconstrained robotics, or dynamic multi-agent interaction
-- a standalone source of original scene assets or video files
-## ⚠️ Limitations and Biases
-Users should be aware of the following limitations:
-- The benchmark is restricted to **indoor scenes**.
-- It focuses specifically on **small object-centric localization and spatial expression**, rather than full-scene understanding.
-- Released QA pairs are constructed from grounded scene geometry and benchmark logic, so some answer styles may be more regular than unconstrained human language.
-- Some target names are preserved as different released **surface forms** even when they map to the same canonical category.
-- The repository does not redistribute original videos or raw scene assets, so reproduction requires separate access to the source datasets.
-## ✅ Quality Assurance
-We use a combination of automatic filtering and manual review to improve dataset accuracy and consistency.
-- Invalid labels and background or structural objects are filtered out.
-- Only target instances satisfying the predefined small-object vocabulary are retained.
-- Questions are generated only for target instances with unique labels within a scene.
-- NRI samples contain four distinct candidate options.
-- FSD answers are constrained to be human-readable and localization-oriented.
-- SSP outputs are required to contain parsable key fields.
-- Iterative manual spot-checking is applied to refine templates and QA logic.
-## 📜 License and Upstream Data Notice
-The **Apache-2.0** license in this repository applies to the released benchmark annotations and intermediate spatial representations in this repository.
-The original scene assets remain subject to the official terms, licenses, and access conditions of **ScanNet++** and **ScanNet v2 / ScanNet200**. Users are responsible for obtaining and using upstream source data in compliance with the corresponding original terms.
-## 🏆 Performance Snapshot
-The table below shows a **representative subset** of overall benchmark results. We report averaged scores across TPV, NRI, FSD, and SSP, where **Avg Micro** is the arithmetic mean of task-level micro scores and **Avg Macro** is the arithmetic mean of task-level macro scores.
-| Rank | Model | Avg Micro | Avg Macro |
-|---|---|---:|---:|
-| 1 | Qwen3-VL-8B-Instruct-SFT | 0.48 | 0.49 |
-| 2 | InternVL3.5-8B-Instruct-SFT | 0.45 | 0.45 |
-| 3 | Kimi K2.5 | 0.42 | 0.44 |
-| 4 | Qwen3-VL-8B-Instruct | 0.39 | 0.40 |
-| 5 | GPT-5.4 | 0.38 | 0.40 |
-For full evaluation details, please refer to the paper and project page.
-## 🔗 Resources
-- **Project Page:** [PinpointQA Project Page](https://rainchowz.github.io/PinpointQA)
-- **GitHub Repository:** [https://github.com/rainchowz/PinpointQA](https://github.com/rainchowz/PinpointQA)
-- **Discussions:** [Hugging Face Discussions](https://huggingface.co/datasets/RainChow/PinpointQA/discussions)
-- **Contact:** [zhouzy1622@mails.jlu.edu.cn](mailto:zhouzy1622@mails.jlu.edu.cn)
-## 📚 Citation
-If you use PinpointQA, please cite:
-```bibtex
-@misc{zhou2026pinpointqa,
-  title={PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos},
-  author={Zhiyu Zhou and Peilin Liu and Ruoxuan Zhang and Luyang Zhang and Cheng Zhang and Hongxia Xie and Wen-Huang Cheng},
-  year={2026},
-  note={ACM Multimedia 2026 Dataset Track submission / project release},
-  url={https://huggingface.co/datasets/RainChow/PinpointQA}
-}
-```

+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- video-text-to-text
+pretty_name: PinpointQA
+tags:
+- benchmark
+- spatial-understanding
+- small-object
+- indoor-scenes
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: validation.jsonl
+  - split: test
+    path: test.jsonl
+---
+# PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos
+[**Project Page**](https://rainchowz.github.io/PinpointQA) | [**Paper**](https://huggingface.co/papers/2604.08991) | [**Code**](https://github.com/rainchowz/PinpointQA)
+> **Important:** This repository releases **benchmark annotations** and **grounded intermediate spatial representations** only. It does **not** redistribute the original scene assets or converted video files.
+## 🧭 Overview
+PinpointQA focuses on a practical question: given a known small object such as a phone, charger, remote, or bottle, can a model determine whether it appears, localize it through nearby references, describe its position precisely, and provide an output that is directly useful for downstream systems?
+In addition to benchmark annotations, this repository also releases grounded **intermediate spatial representations** constructed during scene curation. These files preserve the target-centered local spatial context used to generate the released QA pairs and can support further analysis or the construction of additional grounded tasks.
+## 👀 Task Overview
+PinpointQA is organized as a progressive four-stage benchmark:
+| Task | Name | Goal | Output Format |
+|---|---|---|---|
+| TPV | Target Presence Verification | Determine whether a queried small object appears in the scene | `Yes` / `No` |
+| NRI | Nearest Reference Identification | Identify the nearest reference object to the target, excluding the support surface | Multiple choice |
+| FSD | Fine-Grained Spatial Description | Describe the target location with support surface, nearby references, and centimeter-level distances | Natural language |
+| SSP | Structured Spatial Prediction | Output the same grounded spatial information in structured form | JSON |
+## 📊 Key Statistics
+- **Scenes:** 1,024
+- **QA pairs:** 10,094
+- **Canonical target categories:** 102
+- **Source datasets:** ScanNet++, ScanNet200
+- **Task distribution over all released QA pairs:** TPV 26.47%, NRI 23.10%, FSD 25.08%, SSP 25.34%
+- **Source distribution over all released QA pairs:** ScanNet++ 73.2%, ScanNet200 26.8%
+- **Released splits:** train 6,121 / validation 1,954 / test 2,019
+## 🏷️ Category Naming Note
+PinpointQA contains **102 canonical target categories** at the benchmark-definition level.
+You may notice that the dataset viewer reports **more distinct string values** in the target column. This is expected: some semantically equivalent or near-equivalent names are preserved as **surface forms** in released text fields for readability and compatibility with source annotations or task phrasing. Examples include naming variants such as **`mobile phone`** and **`phone`**.
+When reporting benchmark statistics in the paper and project page, we count categories at the **canonical category** level rather than the raw string-surface level.
+## 🚀 Quick Start
+### Install dependencies
+```bash
+pip install datasets
+```
+### Load the dataset
+```python
+from datasets import load_dataset
+dataset = load_dataset("RainChow/PinpointQA")
+print(dataset)
+print(dataset["train"][0])
+```
+### Access a specific split
+```python
+train_set = dataset["train"]
+val_set = dataset["validation"]
+test_set = dataset["test"]
+```
+### Save the dataset locally
+```python
+from datasets import load_dataset
+dataset = load_dataset("RainChow/PinpointQA")
+dataset.save_to_disk("./PinpointQA_hf")
+```
+## 🗂️ Dataset Organization
+```text
+PinpointQA/
+├── train.jsonl
+├── validation.jsonl
+├── test.jsonl
+├── intermediate_spatial_representations/
+│   ├── scene_xxx.json
+│   ├── scene_yyy.json
+│   └── ...
+└── README.md
+```
+### Released Fields
+- `id`: globally unique sample identifier
+- `scene_id`: scene identifier
+- `source_dataset`: `scannetpp` or `scannet200`
+- `local_sample_id`: scene-local sample index
+- `task`: short task label (`TPV`, `NRI`, `FSD`, `SSP`)
+- `question_type`: original long-form task name
+- `instruction`: task instruction
+- `question`: user-facing question text
+- `choices`: candidate options for NRI, otherwise `null`
+- `answer`: ground-truth answer
+- `target`: queried small object name used in the released sample text
+- `split`: split name
+### Example Record
+```json
+{
+  "id": "scene0000_00_0",
+  "scene_id": "scene0000_00",
+  "source_dataset": "scannet200",
+  "local_sample_id": "0",
+  "task": "TPV",
+  "question_type": "target presence verification",
+  "instruction": "Answer only with exactly one word: Yes or No. Do not add any explanation.",
+  "question": "In the entire scene, did the coffee kettle appear?",
+  "choices": null,
+  "answer": "No",
+  "target": "coffee kettle",
+  "split": "train"
+}
+```
+### Field Notes by Task
+- **TPV:** `answer` is `Yes` or `No`
+- **NRI:** `choices` contains four candidate objects; `answer` is the correct option text
+- **FSD:** `answer` is a natural-language location description
+- **SSP:** `answer` is a JSON-formatted string representing structured spatial grounding
+### Intermediate Spatial Representations
+The `intermediate_spatial_representations/` folder stores the grounded scene-level representations used to instantiate TPV, NRI, FSD, and SSP.
+- Each file corresponds to a scene and is aligned with `scene_id`.
+- These files preserve the target-centered local spatial context used for QA construction.
+- The released content includes grounded information such as target objects, support surfaces, nearby references, and local spatial relations/distances.
+For example, a file such as `scene0000_00.json` corresponds to `scene_id = "scene0000_00"` and provides the grounded scene context from which the released QA samples for that scene are derived.
+## 📐 Spatial Semantics
+### Support Surface vs. Reference Objects
+The **support surface** is the surface that directly supports the target object in the final grounded representation.
+- In **NRI**, the support surface is **excluded** from candidate reference options.
+- In **FSD** and **SSP**, the support surface is retained as a distinct field because it is often a necessary localization anchor.
+- Nearby **references** are additional local objects used to describe or structure the final location of the target.
+Depending on scene semantics and released wording, a surface-like object may appear in text fields as a location anchor, but the benchmark definition still treats **support surface** and **reference objects** as functionally different roles.
+### Distances
+Distances in FSD and SSP are derived from grounded scene geometry and expressed in **centimeters** in the released benchmark outputs.
+## 🧱 Source Data Preparation
+This repository releases **benchmark annotations** and **intermediate spatial representations** only. It does **not** redistribute the original scene assets or converted videos.
+To reproduce video-based experiments, users should first obtain the original assets from the official sources of **ScanNet++** and **ScanNet v2 / ScanNet200**, subject to their respective licenses and access requirements. Note that **ScanNet200** shares the same underlying source data as **ScanNet v2** and mainly differs in annotation parsing and label space, so the video assets used here still come from the **ScanNet v2** RGB-D data.
+### ScanNet++
+- Official website: [ScanNet++](https://scannetpp.mlsg.cit.tum.de/scannetpp/)
+- Obtain access through the official ScanNet++ release.
+- Download the scenes required by your target split or evaluation subset.
+- Match local assets to the released `scene_id` values.
+### ScanNet v2 / ScanNet200
+- Official ScanNet website: [ScanNet](http://www.scan-net.org/)
+- ScanNet200 benchmark documentation: [ScanNet200 Benchmark Documentation](https://kaldir.vc.in.tum.de/scannet_benchmark/documentation)
+- Obtain access to the original data and prepare the scenes required by your pipeline.
+- Match local assets to the released `scene_id` values used in this benchmark.
+### Video Conversion Tools
+The source assets from **ScanNet++** and **ScanNet v2 / ScanNet200** are **not distributed as ready-to-use MP4 videos**. If your pipeline expects standard video files, we provide conversion scripts in the project GitHub repository:
+- `tools/convert_mkv_to_mp4.py`
+- `tools/convert_sens_to_mp4.py`
+Tools folder:
+- [https://github.com/rainchowz/PinpointQA/tree/main/tools](https://github.com/rainchowz/PinpointQA/tree/main/tools)
+### Recommended Local Organization
+```text
+workspace/
+├── PinpointQA/
+│   ├── train.jsonl
+│   ├── validation.jsonl
+│   ├── test.jsonl
+│   └── intermediate_spatial_representations/
+├── raw_data/
+│   ├── scannetpp/
+│   └── scannet200/
+└── videos/
+    ├── scene_or_video_1.mp4
+    ├── scene_or_video_2.mp4
+    └── ...
+```
+Users may organize local files differently depending on their own training or inference pipeline.
+## 🧠 Intended Use
+PinpointQA is intended for:
+- benchmarking multimodal models on small object-centric spatial understanding in indoor videos
+- instruction tuning or supervised fine-tuning for grounded spatial QA tasks
+- studying progressive capability breakdown from target presence to structured spatial output
+- analyzing reference-based localization and spatial grounding behavior in multimodal systems
+## 🚫 Out-of-Scope Use
+PinpointQA is **not** intended as:
+- a general-purpose benchmark for all video understanding abilities
+- a substitute for open-world object tracking or dense video captioning benchmarks
+- a benchmark for outdoor scenes, unconstrained robotics, or dynamic multi-agent interaction
+- a standalone source of original scene assets or video files
+## ⚠️ Limitations and Biases
+Users should be aware of the following limitations:
+- The benchmark is restricted to **indoor scenes**.
+- It focuses specifically on **small object-centric localization and spatial expression**, rather than full-scene understanding.
+- Released QA pairs are constructed from grounded scene geometry and benchmark logic, so some answer styles may be more regular than unconstrained human language.
+- Some target names are preserved as different released **surface forms** even when they map to the same canonical category.
+- The repository does not redistribute original videos or raw scene assets, so reproduction requires separate access to the source datasets.
+## ✅ Quality Assurance
+We use a combination of automatic filtering and manual review to improve dataset accuracy and consistency.
+- Invalid labels and background or structural objects are filtered out.
+- Only target instances satisfying the predefined small-object vocabulary are retained.
+- Questions are generated only for target instances with unique labels within a scene.
+- NRI samples contain four distinct candidate options.
+- FSD answers are constrained to be human-readable and localization-oriented.
+- SSP outputs are required to contain parsable key fields.
+- Iterative manual spot-checking is applied to refine templates and QA logic.
+## 📜 License and Upstream Data Notice
+The **Apache-2.0** license in this repository applies to the released benchmark annotations and intermediate spatial representations in this repository.
+The original scene assets remain subject to the official terms, licenses, and access conditions of **ScanNet++** and **ScanNet v2 / ScanNet200**. Users are responsible for obtaining and using upstream source data in compliance with the corresponding original terms.
+## 🏆 Performance Snapshot
+The table below shows a **representative subset** of overall benchmark results. We report averaged scores across TPV, NRI, FSD, and SSP, where **Avg Micro** is the arithmetic mean of task-level micro scores and **Avg Macro** is the arithmetic mean of task-level macro scores.
+| Rank | Model | Avg Micro | Avg Macro |
+|---|---|---:|---:|
+| 1 | Qwen3-VL-8B-Instruct-SFT | 0.48 | 0.49 |
+| 2 | InternVL3.5-8B-Instruct-SFT | 0.45 | 0.45 |
+| 3 | Kimi K2.5 | 0.42 | 0.44 |
+| 4 | Qwen3-VL-8B-Instruct | 0.39 | 0.40 |
+| 5 | GPT-5.4 | 0.38 | 0.40 |
+For full evaluation details, please refer to the paper and project page.
+## 🔗 Resources
+- **Project Page:** [PinpointQA Project Page](https://rainchowz.github.io/PinpointQA)
+- **GitHub Repository:** [https://github.com/rainchowz/PinpointQA](https://github.com/rainchowz/PinpointQA)
+- **Discussions:** [Hugging Face Discussions](https://huggingface.co/datasets/RainChow/PinpointQA/discussions)
+- **Contact:** [zhouzy1622@mails.jlu.edu.cn](mailto:zhouzy1622@mails.jlu.edu.cn)
+## 📚 Citation
+If you use PinpointQA, please cite:
+```bibtex
+@misc{zhou2026pinpointqa,
+  title={PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos},
+  author={Zhiyu Zhou and Peilin Liu and Ruoxuan Zhang and Luyang Zhang and Cheng Zhang and Hongxia Xie and Wen-Huang Cheng},
+  year={2026},
+  note={ACM Multimedia 2026 Dataset Track submission / project release},
+  url={https://huggingface.co/datasets/RainChow/PinpointQA}
+}
+```