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 | Conference PDF | Archive PDF | Plain Text | 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 {flin2, yyue, zzhang15, shou}@wpi.edu, yamada@tohoku.ac.jp, {vkola, srv}@bu.edu |
| 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. |