Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
Authors: Qianxu Wang, Haotong Zhang, Congyue Deng, Yang You, Hao Dong, Yixin Zhu, Leonidas Guibas
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model through real-world experiments with a robot hand, opting for direct assessment in real-world settings to leverage the superior stability of large vision models like DINO (Caron et al., 2021; Oquab et al., 2023) on real images over synthetic ones. |
| Researcher Affiliation | Academia | 1 CFCS, School of Computer Science, Peking University, China 2 Department of Computer Science, Stanford University, USA 3 Institute for AI, Peking University, China 4 PKU-WUHAN Institute for Artificial Intelligence, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://helloqxwang.github.io/SparseDFF |
| Open Datasets | Yes | Box: A demonstration with the Cheez-It box from the YCB dataset (Calli et al., 2015b; 2017; 2015a) (ID=3) is utilized for initial evaluation. |
| Dataset Splits | No | The paper describes training and testing procedures but does not explicitly state specific training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | After this setup, our feature network takes 20000 iterations for adaptation, roughly 300 seconds using a single NVIDIA Ge Force RTX 3090. Once trained, the network is applied unchanged to different real-world scenes to optimize the hand pose for 300 iterations, roughly 20 seconds using a single NVIDIA Ge Force RTX 3090. |
| Software Dependencies | No | The paper mentions using large vision models like DINO, but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | In our implementation, we set λpen 10 1, λspen 10 2, λpose 10 2. |