Update README.md

README.md

@@ -34,3 +34,450 @@ configs:
   - split: placement
     path: data/placement-*
 ---
+<!-- 新 benchmark 发布公告 -->
+<div style="background-color: #ecfdf5; border-left: 4px solid #10b981; padding: 0.75em 1em; margin-top: 1em; color: #065f46; font-weight: bold; border-radius: 0.375em;">
+  🎉 本仓库为 <strong>RefSpatial</strong> 的新版本 <strong>RefSpatial-Bench-Expand</strong>！<br>
+  新版本在原始基础上<strong>扩充了室内场景</strong>（如工厂、商店），并新增了<strong>未涵盖的室外场景</strong>（如街道、停车场），进一步提升了空间理解任务的多样性与挑战性。
+</div>
+<div style="background-color: #fef3c7; border-left: 4px solid #f59e0b; padding: 0.75em 1em; margin-top: 1em; color: #78350f; font-weight: bold; border-radius: 0.375em;">
+  🏆 本Benchmark所属的文章<strong>RoboRefer</strong> 已被 <strong>NeurIPS 2025</strong> 正式接收！<br>
+  感谢大家的关注与支持！ 🙌
+</div>
+
+
+
+
+
+<h1 style="display: flex; align-items: center; justify-content: center; font-size: 1.75em; font-weight: 600;">
+  
+  <img src="assets/logo.png" style="height: 60px; flex-shrink: 0;">
+  
+  <span style="line-height: 1.2; margin-left: 0px; text-align: center;">
+    RefSpatial-Bench: A Benchmark for Multi-step Spatial Referring
+  </span>
+
+</h1>
+
+
+<!-- # RefSpatial-Bench: A Benchmark for Multi-step Spatial Referring with Reasoning -->
+
+ <!-- [![Generic badge](https://img.shields.io/badge/🤗%20Datasets-BAAI/RefSpatial--Bench-blue.svg)](https://huggingface.co/datasets/BAAI/RefSpatial-Bench)  -->
+ 
+<p align="center">
+  <a href="https://zhoues.github.io/RoboRefer"><img src="https://img.shields.io/badge/%F0%9F%8F%A0%20Project-Homepage-blue" alt="HomePage"></a>
+  &nbsp;
+  <a href="https://arxiv.org/abs/2506.04308"><img src="https://img.shields.io/badge/arXiv-2506.04308-b31b1b.svg?logo=arxiv" alt="arXiv"></a>
+  &nbsp;
+  <a href="https://github.com/Zhoues/RoboRefer"><img src="https://img.shields.io/badge/Code-RoboRefer-black?logo=github" alt="Project Homepage"></a>
+  &nbsp;
+  <a href="https://huggingface.co/datasets/JingkunAn/RefSpatial"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-RefSpatial--Dataset-brightgreen" alt="Dataset"></a>
+  &nbsp;
+  <a href="https://huggingface.co/collections/Zhoues/roborefer-and-refspatial-6857c97848fab02271310b89"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Weights-RoboRefer-yellow" alt="Weights"></a>
+</p>
+
+
+Welcome to **RefSpatial-Bench**, a challenging benchmark based on real-world cluttered scenes to evaluate more complex multi-step spatial referring with reasoning.
+
+<img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fzhoues.github.io&labelColor=%232ccce4&countColor=%230158f9" alt="visitor badge" style="display: none;" />
+<img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fanjingkun.github.io&labelColor=%232ccce4&countColor=%230158f9" alt="visitor badge" style="display: none;" />
+
+
+
+<!-- ## 📝 Table of Contents
+* [🎯 Tasks](#🎯-tasks)
+* [🧠 Reasoning Steps](#🧠-reasoning-steps)
+* [📁 Dataset Structure](#📁-dataset-structure)
+  * [🤗 Hugging Face Datasets Format (data/ folder)](#🤗-hugging-face-datasets-format-data-folder)
+  * [📂 Raw Data Format](#📂-raw-data-format)
+* [🚀 How to Use Our Benchmark](#🚀-how-to-use-our-benchmark)
+  * [🤗 Method 1: Using Hugging Face datasets Library](#🤗-method-1-using-hugging-face-datasets-library)
+  * [📂 Method 2: Using Raw Data Files (JSON and Images)](#📂-method-2-using-raw-data-files-json-and-images)
+  * [🧐 Evaluating Our RoboRefer/RoboPoint](#🧐-evaluating-our-roborefer-model)
+  * [🧐 Evaluating Gemini 2.5 Series](#🧐-evaluating-gemini-25-pro)
+  * [🧐 Evaluating the Molmo Model](#🧐-evaluating-the-molmo-model)
+* [📊 Dataset Statistics](#📊-dataset-statistics)
+* [🏆 Performance Highlights](#🏆-performance-highlights)
+* [📜 Citation](#📜-citation)
+--- -->
+
+## 🎯 Task Split
+- Location Task: This task contains **100** samples, which requires model to predicts a 2D point indicating the **unique target object**.
+
+- Placement Task: This task contains **100** samples, which requires model to predicts a 2D point within the **desired free space**.
+
+- Unseen Set: This set comprises **77** samples from the Location/Placement task, specifically designed to **evaluate model generalization after SFT/RFT training on RefSpatial**, as it includes novel spatial relation combinations not present in RefSpatial.
+
+<div style="background-color: #ffe4e6; border-left: 4px solid #dc2626; padding: 0.75em 1em; margin-top: 1em; color: #b91c1c; font-weight: bold; border-radius: 0.375em;">   ⚠️ Warning: If your model is not trained with RefSpatial, Unseen set should not be used for evaluation. </div>
+
+
+## 🧠 Reasoning Steps
+
+- We introduce *reasoning steps* (`step`) for each benchmark sample as the number of anchor objects and their spatial relations that help constrain the search space.
+- A higher `step` value reflects greater reasoning complexity and a stronger need for spatial understanding and reasoning.
+
+
+## 📁 Dataset Structure
+
+We provide two formats:
+
+<details>
+<summary><strong>Hugging Face Datasets Format</strong></summary>
+
+`data/` folder contains HF-compatible splits:
+
+* `location`
+* `placement`
+* `unseen`
+
+Each sample includes:
+
+| Field    | Description                                                  |
+| :------- | :----------------------------------------------------------- |
+| `id`     | Unique integer ID                                            |
+| `object` | Natural language description of target (object or free area), which is extracted from the `prompt` |
+| `prompt` | Full Referring expressions                                   |
+| `suffix` | Instruction for answer formatting (**different models may use different suffixes or none**; we provide the format used by RoboRefer) |
+| `image`  | RGB image (`datasets.Image`)                                 |
+| `mask`   | Binary mask image (`datasets.Image`)                         |
+| `step`   | Reasoning complexity (number of anchor objects / spatial relations) |
+
+</details>
+
+<details>
+<summary><strong>Raw Data Format</strong></summary>
+
+For full reproducibility and visualization, we also include the original files under:
+
+* `Location/`
+* `Placement/`
+* `Unseen/`
+
+Each folder contains:
+
+```
+Location/
+├── image/        # RGB images (e.g., 0.png, 1.png, ...)
+├── mask/         # Ground truth binary masks
+└── question.json # List of referring prompts and metadata
+```
+
+Each entry in `question.json` has the following format:
+
+```json
+{
+  "id": 40,
+  "object": "the second object from the left to the right on the nearest platform",
+  "prompt": "Please point out the second object from the left to the right on the nearest platform.",
+  "suffix": "Your answer should be formatted as a list of tuples, i.e. [(x1, y1)], ...",
+  "rgb_path": "image/40.png",
+  "mask_path": "mask/40.png",
+  "category": "location",
+  "step": 2
+}
+```
+</details>
+
+
+## 🚀 How to Use RefSpaital-Bench
+
+
+<!-- This section explains different ways to load and use the RefSpatial-Bench dataset. -->
+
+The official evaluation code is available at https://github.com/Zhoues/RoboRefer.
+The following provides a quick guide on how to load and use the RefSpatial-Bench.
+
+
+<details>
+<summary><strong>Method 1: Using Hugging Face Library</strong></summary>
+
+You can load the dataset easily using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset (all splits: location, placement, unseen)
+# This returns a DatasetDict
+dataset_dict = load_dataset("BAAI/RefSpatial-Bench")
+
+# Access a specific split, for example 'location'
+location_split_hf = dataset_dict["location"]
+
+# Or load only a specific split directly (returns a Dataset object)
+# location_split_direct = load_dataset("BAAI/RefSpatial-Bench", name="location")
+
+# Access a sample from the location split
+sample = location_split_hf[0] 
+
+# sample is a dictionary where 'rgb' and 'mask' are PIL Image objects
+# To display (if in a suitable environment like a Jupyter notebook):
+# sample["image"].show()
+# sample["mask"].show()
+
+print(f"Prompt (from HF Dataset): {sample['prompt']}")
+print(f"Suffix (from HF Dataset): {sample['suffix']}")
+print(f"Reasoning Steps (from HF Dataset): {sample['step']}")
+```
+</details>
+
+<details>
+<summary><strong>Method 2: Using Raw Data Files (JSON and Images)</strong></summary>
+
+
+If you are working with the raw data format (e.g., after cloning the repository or downloading the raw files), you can load the questions from the `question.json` file for each split and then load the images and masks using a library like Pillow (PIL).
+
+This example assumes you have the `location`, `placement`, and `unseen` folders (each containing `image/`, `mask/`, and `question.json`) in a known `base_data_path`.
+
+```python
+import json
+import os
+from PIL import Image
+
+# Set the dataset split name and base directory path
+split_name = "Location"
+base_data_path = "."  # Or set to your actual dataset path
+
+# Load question.json file
+question_file = os.path.join(base_data_path, split_name, "question.json")
+try:
+    with open(question_file, 'r', encoding='utf-8') as f:
+        samples = json.load(f)
+except FileNotFoundError:
+    print(f"File not found: {question_file}")
+    samples = []
+
+# Process the first sample if available
+if samples:
+    sample = samples[0]
+    print(f"\n--- Sample Info ---")
+    print(f"ID: {sample['id']}")
+    print(f"Prompt: {sample['prompt']}")
+
+    # Construct absolute paths to RGB image and mask
+    rgb_path = os.path.join(base_data_path, split_name, sample["rgb_path"])
+    mask_path = os.path.join(base_data_path, split_name, sample["mask_path"])
+
+    # Load images using Pillow
+    try:
+        rgb_image = Image.open(rgb_path)
+        mask_image = Image.open(mask_path)
+        sample["image"] = rgb_image
+        sample["mask"] = mask_image
+        print(f"RGB image size: {rgb_image.size}")
+        print(f"Mask image size: {mask_image.size}, mode: {mask_image.mode}")
+    except FileNotFoundError:
+        print(f"Image file not found:\n{rgb_path}\n{mask_path}")
+    except Exception as e:
+        print(f"Error loading images: {e}")
+else:
+    print("No samples loaded.")
+```
+</details>
+
+
+<details>
+<summary><strong>Evaluating RoboRefer / RoboPoint</strong></summary>
+
+To evaluate RoboRefer on RefSpatial-Bench:
+
+1. **Prepare Input Prompt:** 
+
+    Concatenate `sample["prompt"]` and `sample["suffix"]` to form the complete instruction.
+
+   ```python
+   # Example for constructing the full input for a sample
+   full_input_instruction = sample["prompt"] + " " + sample["suffix"]
+   ```
+
+2. **Model Prediction & JSON Parsing & Coordinate Scaling:** 
+
+   - **Model Prediction**: After providingthe image (`sample["image"]`) and `full_input_instruction` to the RoboRefer, it outputs **normalized coordinate in a JSON format** like`[(x, y),...]`, where each `x and `y` value is normalized to a range of 0-1.
+
+   - **JSON Parsing:** Parse this JSON string to extract the coordinate attributes (e.g., `x`, `y`).
+
+   - **Coordinate Scaling:** 
+
+     1. Use `sample["image"].size` to get `(width, height)` and scale to the original image dimensions (height for y, width for x). 
+
+     ```python
+     # Example: model_output_robo is [(0.234, 0.567)] from Roborefer/RoboPoint
+     # sample["image"] is a PIL Image object loaded by the datasets library or loaded from the raw data
+     
+     def text2pts(text, width, height):
+         pattern = r"\(([-+]?\d+\.?\d*(?:,\s*[-+]?\d+\.?\d*)*?)\)"
+         matches = re.findall(pattern, text)
+         points = []
+         for match in matches:
+             vector = [
+                 float(num) if '.' in num else int(num) for num in match.split(',')
+             ]
+             if len(vector) == 2:    
+                 x, y = vector
+                 if isinstance(x, float) or isinstance(y, float):
+                     x = int(x * width)
+                     y = int(y * height)
+                 points.append((x, y))
+     
+     width, height = sample["image"].size
+     scaled_roborefer_points = text2pts(model_output_robo, width, height)
+     
+     # These scaled_roborefer_points are then used for evaluation against the mask.
+     ```
+
+4. **Evaluation:** Compare `scaled_roborefer_points` against `sample["mask"]`. The main metric is **average success rate** — the percentage of predictions falling within the mask.
+
+</details>
+
+<details>
+<summary><strong>Evaluating Gemini Series</strong></summary>
+
+
+To evaluate Gemini Series on RefSpatial-Bench:
+
+1. **Prepare Input Prompt:** 
+
+   Concatenate the string `"Locate the points of"` and `sample["object"] ` to form the complete instruction.
+
+   ```python
+   # Example for constructing the full input for a sample
+   full_input_instruction = "Locate the points of " + sample["object"] + "."
+   ```
+
+2. **Model Prediction & JSON Parsing & Coordinate Scaling:** 
+
+     * **Model Prediction:** After providing the image (`sample["image"]`) and `full_input_instruction` to the Gemini model series, it outputs **normalized coordinates in an JSON format** like `"```json\n[\n  {\"point\": [y, x], \"label\": \"free space\"}, ...\n]\n```"`, where each `y` and `x` value is normalized to a range of 0-1000.
+
+     * **JSON Parsing:** Parse this JSON string to extract the coordinate attributes (e.g., `x1`, `y1`, `x2`, `y2`, etc.).
+
+     * **Coordinate Conversion:** To use these coordinates for evaluation against the mask, they must be:
+       
+       1.  Divided by 1000.0 to normalize them to the 0.0-1.0 range.
+       2.  Scaled to the original image dimensions (height for y, width for x).
+       ```python
+       # Example: model_output_gemini is "```json\n[\n  {\"point\": [438, 330], \"label\": \"free space\"}\n]\n```" from Gemini
+       # and sample["image"] is a PIL Image object loaded by the datasets library or loaded from the raw data
+       
+       def json2pts(text, width, height):
+          match = re.search(r"```(?:\w+)?\n(.*?)```", text, re.DOTALL)
+          if not match:
+              print("No valid code block found.")
+              return np.empty((0, 2), dtype=int)
+      
+          json_cleaned = match.group(1).strip()
+      
+          try:
+              data = json.loads(json_cleaned)
+          except json.JSONDecodeError as e:
+              print(f"JSON decode error: {e}")
+              return np.empty((0, 2), dtype=int)
+      
+          points = []
+          for item in data:
+              if "point" in item and isinstance(item["point"], list) and len(item["point"]) == 2:
+                  y_norm, x_norm = item["point"]
+                  x = int(x_norm / 1000 * width)
+                  y = int(y_norm / 1000 * height)
+                  points.append((x, y))
+      
+          return np.array(points)
+       
+       width, height = sample["image"].size 
+       scaled_gemini_points = json2pts(model_output_gemini, width, height)
+       # These scaled_gemini_points are then used for evaluation against the mask.
+       ```
+
+3. **Evaluation:** Compare `scaled_gemini_points` against `sample["mask"]`. The main metric is **average success rate** — the percentage of predictions falling within the mask.
+
+</details>
+
+<details>
+<summary><strong>Evaluating the Molmo</strong></summary>
+
+To evaluate a Molmo model on this benchmark:
+
+1. **Prepare Input Prompt:** 
+
+   Concatenate `"Locate several points of"` and `sample["object"]` to form the complete instruction.
+
+   ```python
+   # Example for constructing the full input for a sample
+   full_input_instruction = "Locate several points of " + sample["object"] + "."
+   ```
+
+2. **Model Prediction, XML Parsing, & Coordinate Scaling:** 
+
+   - **Model Prediction**: After providing the image (`sample["image"]`) and `full_input_instruction` to the Molmo, it outputs **normalized coordinates in an XML format** like `<points x1="61.5" y1="40.4" x2="76.8" y2="21.8" ... />`, where each `x` and `y` value is normalized to a range of 0-100.
+
+   - **XML Parsing:** Parse this XML string to extract the coordinate attributes (e.g., `x1`, `y1`, `x2`, `y2`, etc.).
+
+   - **Coordinate Conversion:** 
+
+     1.  Divide each coordinate by 100.0 to normalize it to the 0.0-1.0 range.
+     2.  Scaled to the original image dimensions (height for y, width for x). 
+     ```python
+     # Example: model_output_molmo is '<points x1="61.5" y1="40.4" x2="76.8" y2="21.8"/>' from Molmo
+     # and sample["image"] is a PIL Image object loaded by the datasets library or loaded from the raw data
+     
+     def xml2pts(xml_text, width, height):
+     	import re
+         pattern = re.compile(r'(x\d+)="(-?\d+\.?\d*)"\s+(y\d+)="(-?\d+\.?\d*)"')
+         matches = pattern.findall(xml_text)
+         points = [(int(float(x_val) / 100.0 * width), int(float(y_val) / 100.0 * height) ) for _, x_val, _, y_val in matches]
+         return np.array(points)
+     
+     width, height = sample["image"].size 
+     scaled_molmo_points = xml2pts(model_output_molmo, width, height)
+     # These scaled_molmo_points are then used for evaluation.
+     ```
+
+3. **Evaluation:** Compare `scaled_molmo_points` against `sample["mask"]`. The main metric is **average success rate** — the percentage of predictions falling within the mask.
+</details>
+
+
+## 📊 Dataset Statistics
+
+Detailed statistics on `step` distributions and instruction lengths are provided in the table below.
+
+| **RefSpatial-Bench** | **Step / Statistic** | **Samples** | **Avg. Prompt Length** |
+| :------------------- | :------------------- | :---------- | :--------------------- |
+| **Location**         | Step 1               | 30          | 11.13                  |
+|                      | Step 2               | 38          | 11.97                  |
+|                      | Step 3               | 32          | 15.28                  |
+|                      | **Avg. (All)**       | **100**     | 12.78                  |
+| **Placement**        | Step 2               | 43          | 15.47                  |
+|                      | Step 3               | 28          | 16.07                  |
+|                      | Step 4               | 22          | 22.68                  |
+|                      | Step 5               | 7           | 22.71                  |
+|                      | **Avg. (All)**       | **100**     | 17.68                  |
+| **Unseen**           | Step 2               | 29          | 17.41                  |
+|                      | Step 3               | 26          | 17.46                  |
+|                      | Step 4               | 17          | 24.71                  |
+|                      | Step 5               | 5           | 23.8                   |
+|                      | **Avg. (All)**       | **77**      | 19.45                  |
+
+## 🏆 Performance Highlights
+
+As our research shows, **RefSpatial-Bench** presents a significant challenge to current models. In the table below, bold text indicates Top-1 accuracy, and underline text indicates Top-2 accuracy.
+
+|   **Benchmark**    | **Gemini-2.5-Pro** | **SpaceLLaVA** | **RoboPoint** | **Molmo-7B** | **Molmo-72B** | **RoboRefer 2B-SFT** | **RoboRefer 8B-SFT** | **RoboRefer 2B-RFT** |
+| :----------------: | :----------------: | :------------: | :-----------: | :----------: | :-----------: | :------------: | :------------: | :------------: |
+| RefSpatial-Bench-L |    46.96           |      5.82      |     22.87     |    21.91     |     45.77     |  <u>47.00</u>  |   **52.00**    |   **52.00**    |
+| RefSpatial-Bench-P |       24.21        |      4.31      |     9.27      |    12.85     |     14.74     |     48.00      |   <u>53.00</u> |   **54.00**    |
+| RefSpatial-Bench-U |       27.14        |      4.02      |     8.40      |    12.23     |     21.24     |     33.77      |  <u>37.66</u>  |   **41.56**    |
+
+## 📫 Contact
+
+If you have any questions about the benchmark, feel free to email Jingkun (anjingkun02@gmail.com) and Enshen (zhouenshen@buaa.edu.cn).
+<img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fzhoues.github.io&labelColor=%232ccce4&countColor=%230158f9" alt="visitor badge" style="display: none;" />
+<img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fanjingkun.github.io&labelColor=%232ccce4&countColor=%230158f9" alt="visitor badge" style="display: none;" />
+## 📜 Citation
+
+Please consider citing our work if this benchmark is useful for your research.
+
+```
+@article{zhou2025roborefer,
+    title={RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics},
+    author={Zhou, Enshen and An, Jingkun and Chi, Cheng and Han, Yi and Rong, Shanyu and Zhang, Chi and Wang, Pengwei and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and others},
+    journal={arXiv preprint arXiv:2506.04308},
+    year={2025}
+}
+```