2 code implementations • 25 Mar 2024 • Zicong Fan, Takehiko Ohkawa, Linlin Yang, Nie Lin, Zhishan Zhou, Shihao Zhou, Jiajun Liang, Zhong Gao, Xuanyang Zhang, Xue Zhang, Fei Li, Liu Zheng, Feng Lu, Karim Abou Zeid, Bastian Leibe, Jeongwan On, Seungryul Baek, Aditya Prakash, Saurabh Gupta, Kun He, Yoichi Sato, Otmar Hilliges, Hyung Jin Chang, Angela Yao
We interact with the world with our hands and see it through our own (egocentric) perspective.
no code implementations • 11 Dec 2023 • Aditya Prakash, Arjun Gupta, Saurabh Gupta
Objects undergo varying amounts of perspective distortion as they move across a camera's field of view.
no code implementations • 11 Dec 2023 • Aditya Prakash, Ruisen Tu, Matthew Chang, Saurabh Gupta
We present WildHands, a method for 3D hand pose estimation in egocentric images in the wild.
no code implementations • 4 May 2023 • Aditya Prakash, Matthew Chang, Matthew Jin, Saurabh Gupta
Prior works for reconstructing hand-held objects from a single image rely on direct 3D shape supervision which is challenging to gather in real world at scale.
3 code implementations • 31 May 2022 • Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, Andreas Geiger
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Ranked #6 on Autonomous Driving on CARLA Leaderboard
1 code implementation • ICCV 2021 • Kashyap Chitta, Aditya Prakash, Andreas Geiger
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving.
Ranked #4 on Novel View Synthesis on X3D
2 code implementations • CVPR 2021 • Aditya Prakash, Kashyap Chitta, Andreas Geiger
How should representations from complementary sensors be integrated for autonomous driving?
Ranked #1 on Autonomous Driving on Town05 Short
1 code implementation • CVPR 2020 • Aditya Prakash, Aseem Behl, Eshed Ohn-Bar, Kashyap Chitta, Andreas Geiger
Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e. g., learning to drive in a specific simulation condition.
3 code implementations • 20 May 2020 • Aseem Behl, Kashyap Chitta, Aditya Prakash, Eshed Ohn-Bar, Andreas Geiger
Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.
no code implementations • 3 Oct 2018 • Omid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, Serge Belongie
Estimating fundamental matrices is a classic problem in computer vision.
2 code implementations • 3 Aug 2018 • Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V., Aditya Prakash, R. Venkatesh Babu
Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.