├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── SETUP.md ├── docs └── images │ ├── coord_frames.png │ └── dataset_figure_mini.jpg └── notebooks └── OmniNOCS_dataset_visualization.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # Distribution / packaging 7 | .Python 8 | build/ 9 | develop-eggs/ 10 | dist/ 11 | downloads/ 12 | eggs/ 13 | .eggs/ 14 | lib/ 15 | lib64/ 16 | parts/ 17 | sdist/ 18 | var/ 19 | wheels/ 20 | share/python-wheels/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | MANIFEST 25 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # How to Contribute 2 | 3 | ## Contributor License Agreement 4 | 5 | Contributions to this project must be accompanied by a Contributor License 6 | Agreement. 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We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright [yyyy] [name of copyright owner] 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. 203 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OmniNOCS 2 | 3 | This is the official repository for the OmniNOCS dataset. OmniNOCS is a 4 | large-scale NOCS dataset with ground truth annotations for data from different 5 | domains: outdoor scenes, indoor scenes, and object-centric videos. OmniNOCS 6 | contains independent frames, the objects in which are labelled with canonical 3D 7 | orientations, instance masks, and 3D NOCS coordinates. This repository provides 8 | the OmniNOCS annotations for these data sources: [KITTI], [Virtual-KITTI], 9 | [nuScenes], [Waymo-OD], [Cityscapes], [Hypersim], [Objectron], [ARKitScenes], 10 | and [NOCS-Real275]. 11 | 12 | **Quick links:** 13 | [Data](https://console.cloud.google.com/storage/browser/omninocs-dataset) | 14 | [Notebook](./notebooks/OmniNOCS_dataset_visualization.ipynb) 15 | 16 | ![OmniNOCS dataset](./docs/images/dataset_figure_mini.jpg) 17 | 18 | ## Data format 19 | 20 | Each frame in OmniNOCS provides instance segmentations, NOCS coordinates and 3D 21 | bounding boxes in the following format: 22 | 23 | - Instance segmentations: An instance map image stored at 24 | `_instances.png`, as a 16-bit single-channel PNG image. Each 25 | object with a valid mask has its instance ID at each pixel in its mask. The 26 | instance ID is unique to this particular frame. Regions without a valid 27 | object index can either be background (contains 0) or unknown (contains 28 | 65535). Unknown regions should not be used for supervision / evaluation. 29 | 30 | - NOCS coordinates: A NOCS map stored at `_nocs.png` as 31 | a 16-bit 4-channel PNG image. The first 3 channels represent the X,Y,Z NOCS 32 | values. The last channel is a binary mask that denotes whether the pixel 33 | location contains a valid NOCS coordinate. Note that the NOCS annotations in 34 | some domains (particularly outdoor datasets such as KITTI) are sparse, and 35 | therefore all pixels within an object's instance mask need not have valid 36 | NOCS coordinates. 37 | 38 | - 3D bounding box: We provide the 3D bounding box for each object in a 39 | frame in the JSON metadata file (explained below). 40 | 41 | Each OmniNOCS `-` combination (for example `KITTI-train`) has its 42 | own JSON metadata file. Each JSON contains a list of per-frame metadata, with 43 | the length of the list being equal to the number of frames in the particular 44 | combination. For each frame, the metadata has the following structure: 45 | 46 | ``` 47 | 'objects': [ 48 | // A dict for each object in the frame. 49 | { 50 | 'rotation': 3x3 canonical orientation (object to camera transformation), 51 | 'translation': 3x1 3D translation (in meters) (in camera coordinates), 52 | 'size': 3x1 3D size (in meters), 53 | 'object_id': instance ID used in the instance segmentation map, 54 | 'category': name of the object class as a string, 55 | } 56 | ... 57 | ] 58 | 'image_name': Path to the image for this frame in the original dataset. 59 | 'omninocs_name': Path to the NOCS and instance images for this frame in OmniNOCS. 60 | 'nocs_image_downscale': Scalar NOCS downscaling factor (Image resolution / NOCS map resolution), for cases where the NOCS image is smaller than the color image. 61 | 'intrinsics': { 62 | 'fx': Focal length (x) in pixels. 63 | 'fy': Focal length (y) in pixels. 64 | 'cx': Principal point (x) in pixels. 65 | 'cy': Principal point (y) in pixels. 66 | } 67 | ``` 68 | 69 | ## Coordinate convention 70 | 71 | We use right-handed coordinate frames for objects and cameras. 72 | 73 | Object coordinate frame: 74 | 75 | OmniNOCS objects have a per-category canonically oriented frames. This means 76 | that the X, Y and Z axes of all objects in the category are consistently 77 | oriented. For example, cars have the +X axis forwards, +Y axis to their left and 78 | the +Z axis pointing upwards. When objects are placed upright in the scene, 79 | their +Z axis points in the direction opposite to gravity. Examples for a few 80 | object categories are shown below: 81 | 82 | ![object coordinate frames](./docs/images/coord_frames.png) 83 | 84 | There are a few classes with symmetries (eg: bottle or bowl) that have 85 | rotational symmetry about an axis, making the canonical orientation ambiguous. 86 | We are also aware that there are a few objects for which the orientations may 87 | not be canonical, due to occlusions or labelling error. Please report any such 88 | cases you may find, so that they can be removed or corrected. 89 | 90 | Camera coordinate frame: 91 | 92 | Our camera convention uses the +X axis towards right, +Y downwards, and +Z 93 | outwards from the camera. Our metadata files only contain the camera intrinsics 94 | (without extrinsics), since the object pose is already provided with respect to 95 | the camera frame. 96 | 97 | ## Download 98 | 99 | OmniNOCS provides NOCS annotations for images from other datasets. Please refer 100 | to [SETUP.md](./SETUP.md) for instructions to download all data and setup 101 | OmniNOCS. 102 | 103 | Links to download OmniNOCS will be available soon! 104 | 105 | ## Usage 106 | 107 | To visualize our data and illustrate its usage, we provide a colab that 108 | downloads a small mini split of the training set and visualizes the NOCS and 109 | bounding box annotations from OmniNOCS. 110 | [![colab_badge](https://colab.sandbox.google.com/assets/colab-badge.svg)](./notebooks/OmniNOCS_dataset_visualization.ipynb) 111 | 112 | ## Citing this work 113 | 114 | Add citation details here, usually a pastable BibTeX snippet: 115 | 116 | ```latex 117 | @article{omninocs2024}, 118 | title={OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects}, 119 | author={Akshay Krishnan and Abhijit Kundu and Kevis-Kokitsi Maninis and James Hays and Matthew Brown}, 120 | year={2024}, 121 | } 122 | ``` 123 | 124 | ## License and disclaimer 125 | 126 | Copyright 2024 DeepMind Technologies Limited 127 | 128 | All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you 129 | may not use this file except in compliance with the Apache 2.0 license. You may 130 | obtain a copy of the Apache 2.0 license at: 131 | https://www.apache.org/licenses/LICENSE-2.0 132 | 133 | Our dataset adds annotations to images from the datasets listed below, these are 134 | made available with licenses as follows: 135 | 136 | Dataset | License 137 | ------------- | ------- 138 | KITTI | [CC BY-NC-SA 3.0 DEED](https://creativecommons.org/licenses/by-nc-sa/3.0/) 139 | ARKitScenes | [CC BY-NC-SA 4.0 DEED](https://creativecommons.org/licenses/by-nc-sa/4.0/) 140 | Virtual KITTI | [CC BY-NC-SA 3.0 LEGAL CODE](https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode) 141 | nuScenes | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) 142 | Hypersim | [CC BY-NC-SA 3.0 DEED](https://creativecommons.org/licenses/by-sa/3.0/) 143 | NOCS-Real275 | [MIT](https://opensource.org/license/mit) 144 | Waymo OD | [Apache 2.0](https://github.com/waymo-research/waymo-open-dataset/blob/master/LICENSE) ([See Waymo terms](https://waymo.com/open/terms/)) 145 | Objectron | [Microsoft C-UDA](https://github.com/microsoft/Computational-Use-of-Data-Agreement) 146 | Cityscapes 3D | [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en) 147 | 148 | All other materials are licensed under the Creative Commons Attribution 4.0 149 | International License (CC-BY). You may obtain a copy of the CC-BY license at: 150 | https://creativecommons.org/licenses/by/4.0/legalcode 151 | 152 | Unless required by applicable law or agreed to in writing, all software and 153 | materials distributed here under the Apache 2.0 or CC-BY licenses are 154 | distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, 155 | either express or implied. See the licenses for the specific language governing 156 | permissions and limitations under those licenses. 157 | 158 | This is not an official Google product. 159 | 160 | [KITTI]: http://www.cvlibs.net/datasets/kitti/eval_3dobject.php 161 | [Virtual-KITTI]: https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/ 162 | [Objectron]: https://github.com/google-research-datasets/Objectron 163 | [NOCS-Real275]: https://github.com/hughw19/NOCS_CVPR2019 164 | [Cityscapes]: https://www.cityscapes-dataset.com/ 165 | [nuScenes]: https://www.nuscenes.org/ 166 | [Waymo-OD]: https://waymo.com/open/ 167 | [ARKitScenes]: https://github.com/apple/ARKitScenes 168 | [Hypersim]: https://github.com/apple/ml-hypersim 169 | -------------------------------------------------------------------------------- /SETUP.md: -------------------------------------------------------------------------------- 1 | # OmniNOCS setup 2 | 3 | OmniNOCS adds object NOCS annotations to 10 3D datasets: [KITTI], 4 | [Virtual-KITTI], [nuScenes], [Waymo-OD], [Cityscapes], [Hypersim], [Objectron], 5 | [ARKitScenes], and [NOCS-Real275]. 6 | 7 | ## Download Images 8 | 9 | The data release includes the NOCS and 3D object annotations. Users are required 10 | to download the images from the respective datasets. We provide here the list of 11 | sources to download data from below. Using OmniNOCS requires agreeing to the 12 | data policy of these datasets. 13 | 14 | Dataset | Download Links 15 | ------------------ | -------------- 16 | KITTI | [Images](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip) 17 | Virtual KITTI | [Images](http://download.europe.naverlabs.com//virtual_kitti_2.0.3/vkitti_2.0.3_rgb.tar) 18 | nuScenes | [v1.0 trainval camera blobs](https://www.nuscenes.org/nuscenes) 19 | Waymo open dataset | [Perception v2.0.0 images, segmentations](https://waymo.com/open/download/) 20 | Cityscapes | [Left camera Images](https://www.cityscapes-dataset.com/file-handling/?packageID=3) 21 | Hypersim | [.tonemap.jpg images](https://github.com/facebookresearch/omni3d/blob/main/DATA.md#hypersim) 22 | Objectron | [Images](https://github.com/google-research-datasets/Objectron/blob/master/notebooks/Download%20Data.ipynb) 23 | ARKitScenes | [Images](https://github.com/facebookresearch/omni3d/blob/main/DATA.md#arkitscenes) 24 | 25 | [KITTI]: http://www.cvlibs.net/datasets/kitti/eval_3dobject.php 26 | [Virtual-KITTI]: https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/ 27 | [Objectron]: https://github.com/google-research-datasets/Objectron 28 | [NOCS-Real275]: https://github.com/hughw19/NOCS_CVPR2019 29 | [Cityscapes]: https://www.cityscapes-dataset.com/ 30 | [nuScenes]: https://www.nuscenes.org/ 31 | [Waymo-OD]: https://waymo.com/open/ 32 | [ARKitScenes]: https://github.com/apple/ARKitScenes 33 | [Hypersim]: https://github.com/apple/ml-hypersim 34 | 35 | ## Download NOCS annotations 36 | 37 | The NOCS annotations for each dataset are provided through the GCP buckets 38 | below: 39 | 40 | - [OmniNOCS data](https://console.cloud.google.com/storage/browser/omninocs-dataset) 41 | (without the Waymo subset) 42 | - [OmniNOCS-Waymo subset](https://console.cloud.google.com/storage/browser/omni_nocs) 43 | 44 | It is recommended to use the 45 | [gsutil](https://cloud.google.com/storage/docs/gsutil) tool to download them. 46 | 47 | The annotations for each data subset of OmniNOCS are provided as a separate 48 | tar.gz file. Each tar.gz contains a folder named 49 | `omninocs_release_`, which itself contains: 50 | 51 | - a folder with the NOCS images and instance segmentation maps, named after 52 | the subset (eg: kitti_object). 53 | - 3 metadata files `_metadata.json` (one for each split) which 54 | contain path to images, NOCS, segmentation maps, and other object metadata 55 | (pose, category, size) for each example (see 56 | [data format](./README.md#data-format)). 57 | 58 | Note that these annotations are released under licenses as specified in the 59 | [README](./README.md#license-and-disclaimer). 60 | 61 | Move the contents of each OmniNOCS subset to a common `omninocs_root` folder. 62 | This will result in a directory structure as follows: 63 | 64 | ``` 65 | - omninocs_root 66 | - ARKitScenes/ 67 | - Training 68 | - Validation 69 | - ARKitScenes_train_metadata.json 70 | - ARKitScenes_test_metadata.json 71 | - ARKitScenes_val_metadata.json 72 | - Objectron/ 73 | - Objectron_train_metadata.json 74 | - Objectron_test_metadata.json 75 | - Objectron_val_metadata.json 76 | - 77 | ... (and so on for all constituent datasets) 78 | ``` 79 | 80 | The [colab notebook](./notebooks/OmniNOCS_dataset_visualization.ipynb) shows an 81 | example of parsing OmniNOCS data once downloaded. 82 | -------------------------------------------------------------------------------- /docs/images/coord_frames.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/google-deepmind/omninocs/12db9631ff3efb999bdc011ebceea8e841f077a9/docs/images/coord_frames.png -------------------------------------------------------------------------------- /docs/images/dataset_figure_mini.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/google-deepmind/omninocs/12db9631ff3efb999bdc011ebceea8e841f077a9/docs/images/dataset_figure_mini.jpg --------------------------------------------------------------------------------