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@@ -73,6 +73,7 @@ Visit our [project page](https://ldkong.com/Robo3D) to explore more examples. :o
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## Updates
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- \[2023.07\] - Robo3D was accepted to [ICCV 2023](https://iccv2023.thecvf.com/)! :tada:
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- \[2023.03\] - We establish *"Robust 3D Perception"* leaderboards on [Paper-with-Code](https://paperswithcode.com/paper/robo3d-towards-robust-and-reliable-3d): <sup>1</sup>[`KITTI-C`](https://paperswithcode.com/dataset/kitti-c), <sup>2</sup>[`SemanticKITTI-C`](https://paperswithcode.com/dataset/semantickitti-c), <sup>3</sup>[`nuScenes-C`](https://paperswithcode.com/dataset/nuscenes-c), and <sup>4</sup>[`WOD-C`](https://paperswithcode.com/dataset/wod-c). Join the challenge today! :raising_hand:
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- \[2023.03\] - The `KITTI-C`, `SemanticKITTI-C`, and `nuScenes-C` datasets are ready for download at the [OpenDataLab](https://opendatalab.com/) platform. Kindly refer to [this](docs/DATA_PREPARE.md) page for more details on preparing these datasets. :beers:
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- \[2023.01\] - Launch of the `Robo3D` benchmark. In this initial version, we include **12** detectors and **22** segmentors, evaluated on **4** large-scale autonomous driving datasets (KITTI, SemanticKITTI, nuScenes, and Waymo Open) with **8** corruption types across **3** severity levels.

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