no code implementations • 28 Sep 2023 • Alex Zihao Zhu, Jieru Mei, Siyuan Qiao, Hang Yan, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar
Finally, we directly project the superpixel class predictions back into the pixel space using the associations between the superpixels and the image pixel features.
no code implementations • 17 Oct 2022 • Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela, Sean Rafferty, Nicholas Armstrong-Crews, Tiffany Chen, Dragomir Anguelov
Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to camera and elevation is unknown to radar.
no code implementations • 14 Oct 2022 • Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar, Sören Pirk
We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.
1 code implementation • 15 Jun 2022 • Wei-Chih Hung, Henrik Kretzschmar, Vincent Casser, Jyh-Jing Hwang, Dragomir Anguelov
The popular object detection metric 3D Average Precision (3D AP) relies on the intersection over union between predicted bounding boxes and ground truth bounding boxes.
1 code implementation • 15 Jun 2022 • Jieru Mei, Alex Zihao Zhu, Xinchen Yan, Hang Yan, Siyuan Qiao, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar, Dragomir Anguelov
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
no code implementations • 8 Jun 2022 • Longlong Jing, Ruichi Yu, Henrik Kretzschmar, Kang Li, Charles R. Qi, Hang Zhao, Alper Ayvaci, Xu Chen, Dillon Cower, Yingwei Li, Yurong You, Han Deng, CongCong Li, Dragomir Anguelov
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving.
2 code implementations • CVPR 2022 • Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P. Srinivasan, Jonathan T. Barron, Henrik Kretzschmar
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
no code implementations • 16 Jan 2022 • Zhao Chen, Vincent Casser, Henrik Kretzschmar, Dragomir Anguelov
We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions.
1 code implementation • NeurIPS 2020 • Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov
The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.
no code implementations • 18 Aug 2020 • Wei-Chih Hung, Henrik Kretzschmar, Tsung-Yi Lin, Yuning Chai, Ruichi Yu, Ming-Hsuan Yang, Dragomir Anguelov
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars.
no code implementations • CVPR 2020 • Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean Rafferty, Henrik Kretzschmar
In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential.
8 code implementations • CVPR 2020 • Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Sheng Zhao, Shuyang Cheng, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.