Initial Upload

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.las filter=lfs diff=lfs merge=lfs -text
+*.ply filter=lfs diff=lfs merge=lfs -text
+*.rad filter=lfs diff=lfs merge=lfs -text
+*.rrhd filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-4.0 
+pretty_name: Hard Intersection Multimodal Samples
+language:
+  - en
+size_categories:
+  - 1k<n<10k
+task_categories:
+  - image-to-3d
+  - image-classification
+  - image-segmentation
+  - depth-estimation
+  - object-detection
+  - other
+annotations_creators:
+  - expert-generated
+  - human-annotated
+tags:
+  - autonomous-driving
+  - multimodal
+  - lidar
+  - camera
+  - gnss
+  - trajectory
+  - hdmap
+  - lanelet2
+  - opendrive
+  - 3dgs
+  - semantic-segmentation
+  - point-cloud
+  - geospatial
+  - intersection
+  - urban-driving
+  - digital-twin
+  - E2E
+  - simulation
+---
+
+# Hard Intersection Multimodal Samples
+
+## Dataset Summary
+Hard Intersection Multimodal Samples is a curated multimodal dataset of accident-prone urban intersection in Japan for autonomous driving research and development.  
+It provides multi-camera images, trajectory, HD maps, semantic annotations, point cloud data, and 3DGS assets.
+A comprehensive, high-precision multimodal dataset of public road environments captured via an industrial-grade MMS equipped with high-performance IMU, GNSS, LiDAR, and cameras. 
+Features raw sensor data (images, point clouds, trajectories), processed HDMaps, metric 3DGS, and HDMap-derived semantic images/point clouds.
+
+---
+
+## Dataset Description
+### Overview
+This dataset focuses on challenging real-world urban intersection and is designed to support research in perception, mapping, and scene understanding.
+The current release include the following intersection:
+- Takanawadai, Tokyo, Japan  
+- The Takanawadai intersection concentrates multiple adverse driving conditions into a single spot: sensor blind spots at the crest of a hill, an irregular six-way intersection with a sharp curve, and other vehicles crossing centerlines on narrow roads.
+- By creating a 3D model of this real-world, accident-prone location—which easily triggers errors even in human drivers—it serves as the ideal "safety benchmark" to test whether autonomous systems can successfully navigate extreme edge cases.
+- Even in the real world, this intersection historically ranked as the second worst in Tokyo for traffic accidents.
+
+<div style="display:flex; gap:0; margin:0; padding:0;">
+  <img src="./assets/26047a_Record084_260217_055911366_Camera_4.jpg" alt="camera4" style="width:33.333%; display:block; margin:0; padding:0;">
+  <img src="./assets/26047a_Record084_260217_055911366_Camera_0.jpg" alt="camera0" style="width:33.333%; display:block; margin:0; padding:0;">
+  <img src="./assets/26047a_Record084_260217_055911366_Camera_1.jpg" alt="camera1" style="width:33.333%; display:block; margin:0; padding:0;">
+</div>
+
+- The dataset combines:
+  - Six synchronized camera views  
+  - Two synchronized camera views
+  - Trajectory data  
+  - HD maps (OpenDRIVE/RoadRunner/Lanelet2/Vissim)
+  - Semantic annotations  
+  - point cloud data 
+  - 3DGS reconstruction assets  
+---
+
+
+### Key Features
+- Focused on high-risk real-world intersection  
+- Multi-domain support:
+  - sensor domain  
+  - map domain  
+  - semantic projection domain  
+  - reconstruction domain  
+  - 360-degree coverage with six cameras  
+  - Developer-friendly formats: OpenDRIVE/RoadRunner/Lanelet2/Vissim  
+  - LAS point clouds  
+
+---
+
+### Modalities
+#### Raw / Primary Data
+- Six synchronized camera images (Camera0–Camera5)
+- Two synchronized camera images (Camera_1,_+15deg/Camera_2,_+15deg)
+- Point cloud data
+- Trajectory data
+
+#### Derived assets
+- HD maps (OpenDRIVE/RoadRunner/Lanelet2/Vissim)
+- Semantic image labels
+- Semantic point clouds with labels
+- 3DGS reconstruction assets  
+---
+
+### Camera Setup
+- Six synchronized cameras (Camera0–Camera5)
+- Full 360-degree coverage
+
+- Two synchronized cameras (`Camera_1,_+15deg/Camera_2,_+15deg`)
+
+- The file naming convention is `{folder}_{record_name}_{date}_{hhmmssmmm}_Camera_{number}`
+- Camera0 is the front view, Camera1 is the front-right view, Camera2 is the rear-right view, Camera3 is the rear-left view, Camera4 is the front-left view, and Camera5 is the top view.
+- Additionally, for the two synchronized cameras: `Camera_1,_+15deg` represents the front view, and `Camera_2,_+15deg` represents the rear-right view.
+- Although the filename includes “+15deg,” it does not mean that the camera is installed in that direction.
+
+- calibration/images.txt: extrinsic parameters (camera poses for each image)
+- calibration/cameras.txt: intrinsic parameters (camera model and intrinsics)
+- In cameras.txt, for the PINHOLE model, PARAMS[] corresponds to: fx, fy, cx, cy.
+
+---
+
+### Trajectory Data
+- The file naming convention is `{folder}_{record_name}_{date}`
+- The dataset includes recordings in the following order of 26047_Record004, 26047_Record050, 26047a_Record004, and 26047a_Record084.
+- The trajectory data is defined in EPSG:6677.
+
+---
+### HD Map Formats
+![hdmap](assets/lanelet2.png)
+The HD map is provided in the format shown below.
+- **OpenDRIVE(Ver1.4/1.6/1.8)**  
+- **RoadRunner HD Map**  
+- **Lanelet2**  
+- **Vissim**  
+
+---
+
+### Point Cloud Data
+- Provided in LAS format
+- Multi-run aggregated (not single-frame LiDAR)
+- The point cloud data (.las) is defined in EPSG:6677, with elevations given as orthometric heights (above sea level).
+
+- Use cases:
+  - Map-aligned geometry
+  - Semantic projection base
+  - Reconstruction workflows
+---
+
+### Semantic Annotations
+- Semantic Image: COCO JSON format
+- The semantic information of the image data and the corresponding images to which the information is assigned are defined in the COCO JSON format.
+<div style="display:flex; gap:0; margin:0; padding:0;">
+  <img src="./assets/semantic_image_camera4.png" alt="semantic_camera4" style="width:33.333%; display:block; margin:0; padding:0;">
+  <img src="./assets/semantic_image_camera0.png" alt="semantic_camera0" style="width:33.333%; display:block; margin:0; padding:0;">
+  <img src="./assets/semantic_image_camera1.png" alt="semantic_camera1" style="width:33.333%; display:block; margin:0; padding:0;">
+</div>
+
+- Semantic Point Cloud: point-wise (LiDAR)
+![semantic_pointcloud](assets/semantic_point_cloud_data.png)
+- The semantic information of the Semantic Point Cloud is stored in the UserData field of the LAS format, as specified in the table below.
+<table>
+  <thead>
+    <tr>
+      <th>Item</th>
+      <th>Code</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td>road_surface</td><td>11</td></tr>
+    <tr><td>traffic_island</td><td>12</td></tr>
+    <tr><td>solid_white_line</td><td>21</td></tr>
+    <tr><td>dashed_white_line</td><td>22</td></tr>
+    <tr><td>solid_yellow_line</td><td>23</td></tr>
+    <tr><td>dashed_yellow_line</td><td>24</td></tr>
+    <tr><td>double_line</td><td>25</td></tr>
+    <tr><td>straight_arrow</td><td>31</td></tr>
+    <tr><td>left_arrow</td><td>32</td></tr>
+    <tr><td>right_arrow</td><td>33</td></tr>
+    <tr><td>left_and_straight_arrow</td><td>34</td></tr>
+    <tr><td>right_and_straight_arrow</td><td>35</td></tr>
+    <tr><td>pedestrian_crossing</td><td>41</td></tr>
+    <tr><td>stop_bar</td><td>42</td></tr>
+    <tr><td>traffic_calming_strip</td><td>43</td></tr>
+    <tr><td>bus</td><td>44</td></tr>
+    <tr><td>vertical_two_traffic_light</td><td>51</td></tr>
+    <tr><td>horizontal_three_traffic_light</td><td>52</td></tr>
+    <tr><td>horizontal_four_traffic_light</td><td>53</td></tr>
+    <tr><td>wrong_way</td><td>61</td></tr>
+    <tr><td>interstate_route</td><td>62</td></tr>
+    <tr><td>other_blue</td><td>63</td></tr>
+    <tr><td>other</td><td>71</td></tr>
+  </tbody>
+</table>
+
+- These annotations are assigned based on HD Map attributes, and objects not defined in the HD Map, such as vehicles, are not annotated.
+
+---
+### 3DGS and Reconstruction Assets
+-	3DGS assets are included as reconstruction-oriented scene representations.
+-	These assets are intended for scene reconstruction, scene visualization, synthetic data preparation, simulation-oriented environment understanding, and map-aligned scene asset generation.
+- 3DGS assets are defined in the same coordinate system as the point cloud data.
+- Data Collection Constraints: Constructed clean, static data by completely filtering out dynamic objects from highly congested public roads with constant vehicle and pedestrian traffic.
+- Environmental Complexity: Accurately reproduced the geometry of complex, dynamic environments—where standard 3DGS generation typically fails—by integrating high-precision LiDAR point clouds captured for HDMap creation.
+- System Scale: Scaled beyond single objects to cover large-scale road networks through an integrated image and point cloud pipeline. Furthermore, when paired with our HDMap data, it functions as a "Metric 3DGS" capable of precise real-world physical dimensions and positioning.
+
+![3dgs](assets/3dgs.png)
+[Please refer to the following viewer for 3DGS assets.](https://huggingface.co/spaces/dynamic-maps/hard-intersection-3dgs-sample)
+
+---
+## Dataset Structure
+```text
+Dataset
+├─annotation
+│  ├─semantic_images
+│  └─semantic_pointcloud
+├─calibration
+├─images
+├─maps
+│  ├─lanelet2
+│  ├─opendrive
+│  ├─roadrunnerhdmap
+│  └─vissim
+│      └─images
+├─pointcloud
+├─reconstruction
+└─trajectory
+```
+---
+
+## Intended Uses
+- multi-camera perception research
+-	map-aware perception
+- semantic segmentation
+- map projection workflows
+- difficult-scene localization
+- trajectory analysis
+- point cloud semantic research
+- panorama-based perception research
+- reconstruction and 3D scene asset research
+- synthetic data or simulation preparation workflows
+
+---
+
+## Social Impact of Dataset
+ - It is expected that an increase in the volume of this dataset will contribute to the following: Improvement of autonomous driving technology.
+ - Reduction in storage capacity through the elimination of duplicate datasets.
+
+
+## Limitations
+- Limited to 1 intersection (not large-scale coverage)
+- LAS is aggregated multi-run data
+- Representation differences across formats
+- Images have been processed to protect personal information: faces have been mosaicked, and license plates have been masked as much as possible, although the masking may not be complete. Users may contact us if additional anonymization is required.
+- The semantic images are not provided for all images. Users may contact us if additional annotation is required.
+
+---
+
+## Difficulty Tags
+- occlusion_heavy  
+- dense_traffic  
+- complex_lane_topology  
+- multi_phase_signal  
+- unprotected_turn  
+
+---
+
+## Citation
+```bibtex
+@dataset{hard_intersection_multimodal_samples_2026,
+  title={Hard Intersection Multimodal Samples},
+  author={Dynamic Map Platform Co., Ltd.},
+  year={2026},
+  publisher={Hugging Face}
+}
+```
+
+## Acknowledgements / Attribution
+This dataset was generated using several open-source models and libraries. We gratefully acknowledge the contributions of the original authors.
+
+### Models
+- **Grounding DINO**  
+  Liu et al., IDEA-Research, ECCV 2024  
+  License: Apache License 2.0  
+  https://github.com/IDEA-Research/GroundingDINO
+
+- **OneFormer**  
+  Jain et al., SHI-Labs, CVPR 2023  
+  License: MIT License  
+  https://github.com/SHI-Labs/OneFormer
+
+- **ViTMatte**  
+  Yao et al., HUST Vision Lab, Information Fusion 2024  
+  License: Apache License 2.0  
+  https://github.com/hustvl/ViTMatte
+
+### Pretrained Models
+- **Grounding DINO Base**  
+  IDEA-Research  
+  License: Apache License 2.0  
+  https://huggingface.co/IDEA-Research/grounding-dino-base
+
+- **OneFormer Cityscapes Swin-L**  
+  SHI-Labs  
+  License: MIT License  
+  https://huggingface.co/shi-labs/oneformer_cityscapes_swin_large
+
+- **ViTMatte Base (Distinctions-646)**  
+  HUST Vision Lab  
+  License: Apache License 2.0  
+  https://huggingface.co/hustvl/vitmatte-base-distinctions-646
+
+### 3D Gaussian Splatting Libraries
+- **gsplat**  
+  Ye, Li et al., UC Berkeley / Nerfstudio, JMLR 2025  
+  License: Apache License 2.0  
+  https://github.com/nerfstudio-project/gsplat
+
+- **Splatfacto-W**  
+  Xu et al., UC Berkeley / ShanghaiTech, arXiv 2024  
+  License: Apache License 2.0  
+  https://github.com/KevinXu02/splatfacto-wyy
+
+---
+
+This dataset contains **only derived data outputs** generated using the above tools.  
+No original model weights or source code are redistributed.
+
+All rights and licenses of the original works remain with their respective authors.
+
+### Redistribution Notice
+
+This repository distributes dataset artifacts only.
+It does NOT include:
+- source code of the above models
+- pretrained model weights
+- third-party libraries
+
+Users must obtain those components separately from their original sources and comply with their respective licenses.
+
+## Feedback/Contact
+- Feedback is optional, but very welcome.
+- Contact: opensource@dynamic-maps.co.jp

annotation/semantic_pointcloud/jp_tokyo_takanawadai_class.las

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calibration/cameras.txt

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+# Camera list with one line of data per camera:
+#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
+# Number of cameras: 33
+1 PINHOLE 4112 3008 2298.003152150404 2298.003152150404 1997.494 1480.124
+2 PINHOLE 4104 2992 2295.5568818157526 2295.5568818157526 2040.359 1493.427
+3 PINHOLE 2048 2464 942.9103885 942.9103885 1012.480064 1212.360282
+4 PINHOLE 2048 2464 947.0767 947.0767 1013.168386 1216.460255
+5 PINHOLE 2048 2464 936.8167592 936.8167592 1023.238171 1222.381327
+6 PINHOLE 2048 2464 944.0099983 944.0099983 1017.279371 1202.426403
+7 PINHOLE 2048 2464 941.644451 941.644451 1014.61384 1215.855179
+8 PINHOLE 2048 2464 944.8994954 944.8994954 1015.86222 1245.573087
+9 PINHOLE 4112 3008 2298.003152150404 2298.003152150404 1997.494 1480.124
+10 PINHOLE 4104 2992 2295.5568818157526 2295.5568818157526 2040.359 1493.427
+11 PINHOLE 2048 2464 942.9103885 942.9103885 1012.480064 1212.360282
+12 PINHOLE 2048 2464 947.0767 947.0767 1013.168386 1216.460255
+13 PINHOLE 2048 2464 936.8167592 936.8167592 1023.238171 1222.381327
+14 PINHOLE 2048 2464 944.0099983 944.0099983 1017.279371 1202.426403
+15 PINHOLE 2048 2464 941.644451 941.644451 1014.61384 1215.855179
+16 PINHOLE 2048 2464 944.8994954 944.8994954 1015.86222 1245.573087
+17 PINHOLE 4112 3008 2298.003152150404 2298.003152150404 1997.494 1480.124
+18 PINHOLE 4104 2992 2295.5568818157526 2295.5568818157526 2040.359 1493.427
+19 PINHOLE 2048 2464 942.9103885 942.9103885 1012.480064 1212.360282
+20 PINHOLE 2048 2464 947.0767 947.0767 1013.168386 1216.460255
+21 PINHOLE 2048 2464 936.8167592 936.8167592 1023.238171 1222.381327
+22 PINHOLE 2048 2464 944.0099983 944.0099983 1017.279371 1202.426403
+23 PINHOLE 2048 2464 941.644451 941.644451 1014.61384 1215.855179
+24 PINHOLE 2048 2464 944.8994954 944.8994954 1015.86222 1245.573087
+25 PINHOLE 4112 3008 2298.003152150404 2298.003152150404 1997.494 1480.124
+26 PINHOLE 4104 2992 2295.5568818157526 2295.5568818157526 2040.359 1493.427
+27 PINHOLE 2048 2464 942.9103885 942.9103885 1012.480064 1212.360282
+28 PINHOLE 2048 2464 947.0767 947.0767 1013.168386 1216.460255
+29 PINHOLE 2048 2464 936.8167592 936.8167592 1023.238171 1222.381327
+30 PINHOLE 2048 2464 944.0099983 944.0099983 1017.279371 1202.426403
+31 PINHOLE 2048 2464 941.644451 941.644451 1014.61384 1215.855179
+32 PINHOLE 2048 2464 944.8994954 944.8994954 1015.86222 1245.573087