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..
BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation
Authors: Zelin Xu, Yichen Zhang, Ke Chen, Kui Jia
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three popularly benchmarking datasets can verify that our method can achieve state-of-the-art performance, especially for the more challenging severe occluded scenes. |
| Researcher Affiliation | Academia | Zelin Xu1 , Yichen Zhang1 , Ke Chen1,2, and Kui Jia1,2, 1South China University of Technology 2Peng Cheng Laboratory EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes are available at https://github.com/Gorilla-Lab-SCUT/Bi Co-Net. |
| Open Datasets | Yes | To evaluate our Bi Co-Net comprehensively, experiments are conducted on three popular benchmarks the YCB-Video dataset [Xiang et al., 2018], the Line MOD [Hinterstoisser et al., 2011], and the more challenging Occlusion Line MOD [Brachmann et al., 2014]. |
| Dataset Splits | No | The paper specifies training and testing splits for the datasets but does not explicitly detail a separate validation dataset split (e.g., in terms of percentages or counts). |
| Hardware Specification | Yes | As a result, the average time for processing a frame for inference is 75ms with a GTX 1080 Ti GPU |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not specify any software libraries with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The numbers of scene/model points, i.e. , N/M, are set to 1000/1000. In point-pair pose computation, we downsample the scene points and model points to Z = 100 points by the FPS... The hyper-parameter λ in the losses of BCM-S and BCM-M branches is empirically set to 0.05. We use the Adam optimizer with a 10 4 learning rate to train our model for 50 epochs, and the learning rate decays 0.3 per 10 epochs. |