gradio_show.md

# 📘 MMScan Hierarchical VIsual Grounding Challenge

![My Result](MMScan_teaser.png)
## 🔍 Challenge Introduction

**Hierarchical Visual Grounding (HVG) Task in the MMScan Benchmark**:  
This task evaluates a model’s ability to perform visual grounding at multiple levels of granularity — from region to object-level, and from single-target localization to inter-targets localization. Given a natural language description, models are expected to accurately locate the corresponding object(s) within the 3D scenes, reflecting comprehensive spatial and attribute-level understanding.

- **Overview**: You can refer to this [website](https://neurips.cc/virtual/2024/poster/97429) for an overview and our [paper](https://arxiv.org/abs/2406.09401) for more details.
- **Challenge Data and Codebase**: The challenge dataset includes:
  - **Training set**: Language prompts + ground-truth bounding boxes  
  - **Validation set**: Language prompts + ground-truth bounding boxes  
  - **Test set**: Language prompts only (no ground truth provided)  
  Follow the [instructions](https://github.com/OpenRobotLab/EmbodiedScan/tree/mmscan) to get familiar with data organization and MMScan APIs. All the code for MMScan is available [here](https://github.com/OpenRobotLab/EmbodiedScan/tree/mmscan).

- **Evaluation Metrics**: For the visual grounding task, our evaluator computes multiple metrics including [email protected] (Average Precision), [email protected], and [email protected] where the gTop-k metric is an expanded metric that generalizes the traditional Top-k metric, offering superior flexibility and interpretability compared to traditional ones when oriented towards multi-target grounding.

- **Contact**: For any questions related to the HVG challenge, feel free to reach out to [**Jingli Lin**](linjingli166@gmail).

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## 📝 How to Participate

To register for the challenge, please contact us via [**Google Mail**](linjingli166@gmail) and include the following information:

- A **self-chosen username** (this will be shown on the leaderboard)  
- A **login password**  
- Your **team or institution name**  
- A brief statement on your **motivation for participating**

> 📌 **Submission limit**: Each user is allowed a **maximum of 5 submissions per day**.
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## 🚀 Submission Guidelines

- Your submission should be a **dictionary**, where each key is a **sample ID** from the test split.
- For each sample, provide:
  - `pred_bboxes`: a list of predicted bounding boxes
  - `scores`: the corresponding confidence scores
- An expected result is:

    ```python
    {
        'VG_Inter_Space_OO__1mp3d_0009_region0__55'(sample ID):
        {
            'pred_bboxes'(list, 100*9): [[...],...]
            'scores'(list, 100): [...]
        }
    ...
    }
    ```

> 💡 **Note**: The bounding boxes do **not** need to be sorted by confidence.

- ⛔ **Limit the number of predicted boxes to 100 per sample**. If your submission contains more than 100 boxes for a single sample, only the top 100 will be considered.

- ⏱️ **Efficiency Tip**: Round all floating-point numbers in your submission to **two decimal places** to reduce file size and transmission overhead. (To ensure fairness during evaluation, all decimal numbers in the submitted predictions will be rounded to two decimal places.)