no code implementations • 14 Dec 2023 • Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i. e., noise levels).
no code implementations • 8 Oct 2023 • Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu
In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.
no code implementations • 23 Jul 2023 • Huijie Zhang, Anthony Opipari, Xiaotong Chen, Jiyue Zhu, Zeren Yu, Odest Chadwicke Jenkins
TransNet is evaluated in terms of pose estimation accuracy on a large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach.
no code implementations • 22 Aug 2022 • Huijie Zhang, Anthony Opipari, Xiaotong Chen, Jiyue Zhu, Zeren Yu, Odest Chadwicke Jenkins
TransNet is evaluated in terms of pose estimation accuracy on a recent, large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach.
1 code implementation • 8 Mar 2022 • Xiaotong Chen, Huijie Zhang, Zeren Yu, Anthony Opipari, Odest Chadwicke Jenkins
Transparent objects are ubiquitous in household settings and pose distinct challenges for visual sensing and perception systems.
1 code implementation • 1 Mar 2022 • Xiaotong Chen, Huijie Zhang, Zeren Yu, Stanley Lewis, Odest Chadwicke Jenkins
We demonstrate the effectiveness of ProgressLabeller by rapidly create a dataset of over 1M samples with which we fine-tune a state-of-the-art pose estimation network in order to markedly improve the downstream robotic grasp success rates.