Datasets:

vevenom
/

SCANnotateDataset

	# SCANnotateDataset
	For up-to-date information please visit our [github repository](https://github.com/stefan-ainetter/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="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="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}
	}
	```