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README.md

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+# SCANnotateDataset
+CAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated 
+using [scannotate](https://github.com/stefan-ainetter/SCANnotate) and [HOC-Search](https://arxiv.org/abs/2309.06107). 
+The quality of these annotations was verified in several verification passes, 
+with manual re-annotations performed for outliers to ensure that final annotations are of high quality. 
+
+<p align="center">
+<img src="https://github.com/stefan-ainetter/SCANnotateDataset/tree/master/figures/example_annotation.png" width="100%"/>
+</p>
+
+## Details about Annotations
+
+For the public [ScanNet dataset](http://www.scan-net.org/), we provide:
+
+* `18617` CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to [Scan2CAD](https://github.com/skanti/Scan2CAD))
+* Accurate 9D pose for each CAD model
+* 3D semantic object instance segmentation corresponding to the annotated objects
+* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet
+* Extracted view parameters (selected RGB-D images and camera poses) for each object, which
+can be used for CAD model retrieval via render-and-compare
+
+## CAD Model and Pose Annotations
+Our annotations for ScanNet are provided as `.pkl` files, which 
+contain additional information about the annotated objects, e.g. view parameters for render-and-compare and the 
+corresponding 3D instance segmentation of the pointcloud data.
+
+For convenience, we additionally provide the annotations as `.json` file using the scan2cad data format. 
+
+**Note** that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet 
+CAD models (center and scale-normalize all CAD models) as explained below, 
+to generate clean CAD models which are then compatible with our annotations.
+
+
+### Preliminaries: Download ShapeNet and ScanNet examples
+
+* Download the ScanNet example scene [here](https://files.icg.tugraz.at/f/5b1b756a78bb457aafb5/?dl=1). Extract the data
+and copy them to `/data/ScanNet/scans`. Note that by downloading this example data
+you agree to the [ScanNet Terms of Use](https://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf). 
+To download the full ScanNet dataset follow the instructions on the [ScanNet GitHub page](https://github.com/ScanNet/ScanNet).
+
+* Download the [ShapenetV2](https://shapenet.org/) dataset by signing up
+on the website. Extract ShapeNetCore.v2.zip to `/data/ShapeNet`.
+
+* Download our annotations for the full ScanNet dataset 
+[here](https://files.icg.tugraz.at/f/249aa5c3418f4c1897ee/?dl=1). Extract the data and copy them to
+`/data/ScanNet/annotations`.
+
+#### Preprocessing ShapeNet CAD Models
+To center and scale-normalize the downloaded ShapeNet CAD models, run:
+```bash
+bash run_shapenet_prepro.sh gpu=0
+```
+The `gpu` argument specifies which GPU should be used for processing. 
+By default, code is executed on CPU.
+
+After the above-mentioned steps the `/data` folder should contain the following directories:
+```text
+- data
+    - ScanNet
+        - annotations
+            - scene0495_00
+            - ...
+        - scans
+            - scene0495_00
+    - ShapeNet
+        - ShapeNet_preprocessed            
+        - ShapeNetCore.v2
+```
+
+#### Installation Requirements and Setup
+
+* Clone this repository. Install PyTorch3D by following the instructions from the
+[official installation guide](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md).
+
+After installing Pytorch3D, run the following command:
+```bash
+pip install scikit-image matplotlib imageio plotly opencv-python open3d trimesh==3.10.2
+```
+
+### Annotations in Scan2CAD data format
+Annotations in scan2cad format are available [here](https://files.icg.tugraz.at/f/aaaf656e64014745af15/?dl=1).
+The file `full_annotions_scannotate.json` contains `1513` entries, where the field of one entry is described as:
+```javascript
+[{
+id_scan : "scannet scene id",
+trs : { // <-- transformation from scan space to world space 
+
+    translation : [tx, ty, tz], // <-- translation vector
+    rotation : [qw, qx, qy, qz], // <-- rotation quaternion
+    scale :  [sx, sy, sz], // <-- scale vector
+    },
+aligned_models : [{ // <-- list of aligned models for this scene
+    sym : "(__SYM_NONE, __SYM_ROTATE_UP_2, __SYM_ROTATE_UP_4 or __SYM_ROTATE_UP_INF)", // <-- symmetry property only one applies
+    catid_cad  : "shapenet category id",
+    id_cad : "shapenet model id",
+    category_name : "", // e.g. chair,
+    trs : { // <-- transformation from CAD space to world space 
+        translation : [tx, ty, tz], // <-- translation vector
+        rotation : [qw, qx, qy, qz], // <-- rotation quaternion
+        scale : [sx, sy, sz] // <-- scale vector
+	},
+    keypoints_scan : {}, // no keypoints in our annotations
+    keypoints_cad : {}, // no keypoints in our annotations
+    scannet_category_label: "", // e.g. chair; this label is taken from original ScanNet 3D object instance segmentation
+    object_id: "", // unique id for each annotated object in the scene
+    is_in_scan2cad: // <-- True if CAD annotation is available in scan2cad, else False
+    }]
+},
+{ ... },
+{ ... },
+]
+```
+
+
+### Visualization of Annotations
+Use the following command to visualize the annotations: 
+```bash
+bash visualize_annotations.sh
+```
+
+## ShapeNet Object Symmetry Annotations
+Automatically generated symmetry tags for all CAD models of considered categories are available for download 
+[here](https://files.icg.tugraz.at/f/58469ba8edbd419abb6d/?dl=1). Symmetry
+tags are saved in the following format:
+```javascript
+[{
+    cad_symmetry_dict: {  // Symmetry Tags for CAD models
+        synset_id: { // shapenet category id, 
+            category_name: "", // e.g. chair,
+            synset_id: "",
+            object_sym_dict: { // <-- dictionary containing CAD model ids and corresponding symmetry tags
+                'id_cad': 'symmetry_tag',
+            },
+    {...},
+    {...},
+}
+}
+}]
+```
+
+To predict the symmetry tag for a given CAD model, we first render depth maps from 6 different views of the 
+preprocessed CAD model. 
+We then rotate the object around the vertical axis by a specific angle (e.g. 180° to check for 
+__SYM_ROTATE_UP_2), and again render the depth maps of the 6 views. If the difference of depth renderings is below a 
+certain threshold, we assume that the object is symmetric according to the performed rotation.
+
+<p align="center">
+<img src="https://github.com/stefan-ainetter/SCANnotateDataset/tree/master/figures/example_symmetry_annotation.png" width="80%"/>
+</p>
+
+## Citation
+To create these annotations, we used the CAD model retrieval pipeline from 
+[scannotate](https://github.com/stefan-ainetter/SCANnotate), but replaced the exhaustive
+CAD retrieval stage with [HOC-Search](https://arxiv.org/abs/2309.06107). 
+If you use any of the provided code or data, please cite the following works:
+
+Scannotate:
+```bibtex
+@inproceedings{ainetter2023automatically,
+  title={Automatically Annotating Indoor Images with CAD Models via RGB-D Scans},
+  author={Ainetter, Stefan and Stekovic, Sinisa and Fraundorfer, Friedrich and Lepetit, Vincent},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
+  pages={3156--3164},
+  year={2023}
+}
+```
+HOC-Search:
+```bibtex
+@misc{ainetter2023hocsearch,
+      title={HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans}, 
+      author={Stefan Ainetter and Sinisa Stekovic and Friedrich Fraundorfer and Vincent Lepetit},
+      year={2023},
+      eprint={2309.06107},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```