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..
InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion
Authors: Fangzhou Lin, Yun Yue, Ziming Zhang, Songlin Hou, Kazunori Yamada, Vijaya Kolachalama, Venkatesh Saligrama
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments for point cloud completion using Info CD and observe significant improvements consistently over all the popular baseline networks trained with CD-based losses, leading to new state-of-the-art results on several benchmark datasets. |
| Researcher Affiliation | Collaboration | Fangzhou Lin1,2 Yun Yue1 Ziming Zhang1 Songlin Hou1,3 Kazunori D Yamada2 Vijaya B Kolachalama4 Venkatesh Saligrama4 1Worcester Polytechnic Institute, USA 2Tohoku University, Japan 3Dell Technologies, USA 4Boston University, USA EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Demo code is available at https://github.com/Zhang-VISLab/Neur IPS2023-Info CD. |
| Open Datasets | Yes | Datasets. We conducted experiments for point cloud completion on the following datasets: PCN [10]: This is a subset of Shape Net [66]... Multi-view partial point cloud (MVP) [67]: This dataset covers 16 categories... Shape Net-55/34 [16]: Shape Net-55 contains 55 categories... Shape Net-Part [65]: This is a subset of Shape Net Core [66]... |
| Dataset Splits | No | The paper provides training and testing splits for several datasets (MVP, Shape Net-55, Shape Net-34), but does not explicitly mention or detail a separate validation split for reproducibility. |
| Hardware Specification | Yes | We conducted our experiments on a server with 4 NVIDIA A100 80G GPUs and one with 10 NVIDIA Quadro RTX 6000 24G GPUs due to the large model sizes of some baseline networks. |
| Software Dependencies | No | The paper mentions using "Py Torch" and optimizers "Adam [74] or Adam W [75]" but does not provide specific version numbers for these or any other software components, which is necessary for reproducible ancillary software details. |
| Experiment Setup | Yes | Hyperparameters such as learning rates, batch sizes and balance factors in the original losses for training baseline networks were kept consistent with the baseline settings for fair comparisons. Hyperparameter τ in Info CD was tuned based on grid search, while λ was set to 10 7 for all the experiments. |