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Found 6 papers, 2 papers with code
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures
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).
The Emergence of Reproducibility and Consistency in Diffusion Models
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.
TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
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.
TransNet: Category-Level Transparent Object Pose Estimation
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.
ClearPose: Large-scale Transparent Object Dataset and Benchmark
Transparent objects are ubiquitous in household settings and pose distinct challenges for visual sensing and perception systems.
ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception
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.
