PIE-NET: Parametric Inference of Point Cloud Edges
Authors: Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train and evaluate our method on the ABC dataset, the largest publicly available dataset of CAD models, via ablation studies and compare our results to those produced by traditional (non-learning) processing pipelines, as well as a recent deep learning-based edge detector (ECNet). |
| Researcher Affiliation | Collaboration | 1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University 2Simon Fraser University 3National University of Defense Technology 4Google Research 5Tsinghua University |
| Pseudocode | No | The paper describes the network architecture and mathematical formulations but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We train PIE-NET on the recently released ABC dataset by [4], which is composed of more than one million feature-rich CAD models with parametric edge representations. The ABC dataset [46], a large-scale CAD mechanical part dataset containing ground-truth annotations for edges and corner points. |
| Dataset Splits | No | The paper mentions training and evaluating on the ABC dataset but does not explicitly provide the specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | Yes | Training times for point classification,the open and closed curve proposal networks were about 23, 12, and 8 hours, respectively, for 100 epochs, on an NVIDIA TITIAN X GPU. |
| Software Dependencies | No | The paper mentions software components and algorithms like Point Net++, focal loss [48], and smooth L1 loss [49, 44] but does not specify their version numbers. |
| Experiment Setup | Yes | We set the parameters τe=0.7 and τc=0.9 throughout our experiments. We set wm=1, wc=1, and wp=10 throughout our experiments. We use τγ=0.6 and τiou=0.6 for all our experiments. |