SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision
Authors: Kseniya Cherenkova, Elona Dupont, Anis Kacem, Gleb Gusev, Djamila Aouada
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To train Spels Net with the proposed point-to-BRep adjacency supervision, we extend two existing CAD datasets with the required annotations, and conduct a thorough experimental validation on them. The obtained results showcase the efficacy of Spels Net and its topological supervision compared to a set of baselines and state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Kseniya Cherenkova Sn T, University of Luxembourg, Artec3D kseniya.cherenkova@uni.lu Elona Dupont Sn T, University of Luxembourg elona.dupont@uni.lu Anis Kacem Sn T, University of Luxembourg anis.kacem@uni.lu Gleb Gusev Artec3D gleb@artec3d.com Djamila Aouada Sn T, University of Luxembourg djamila.auoada@uni.lu |
| Pseudocode | No | The paper describes the network architecture and components in text and with a diagram (Figure 2), but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The code with the network architecture and training can not be currently released under an open-source license that allows others to use, modify, and distribute it due to specifics of the industrial collaboration in the scope of which the work has been done. |
| Open Datasets | Yes | Extended versions of two existing CAD datasets ABCParts Li et al. [2019] and CC3D Cherenkova et al. [2020]. The new versions, called ABC-VEF and CC3D-VEF, include the proposed LAR-based point-to-BRep adjacency representation on the point clouds and will be made publicly available to enable further research; |
| Dataset Splits | Yes | ABCParts-VEF Dataset: Spels Net is trained and evaluated on the ABCParts dataset Li et al. [2019] using the same train (22k), test (3.5k) and validation (3.5k) splits. |
| Hardware Specification | Yes | The training takes approximately 10 days on a node with 4 Nvidia A100(40Gb) GPUs. |
| Software Dependencies | No | The paper mentions software components like 'Sparse CNN encoder' and 'Adam W solver' and 'HDBScan', but it does not specify version numbers for any of these or other underlying software dependencies (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | The input point cloud is normalized to unit sphere, randomly rotated and discretized on a voxel grid with a resolution ρ = 0.01. Spels Net is trained with Adam W solver with a cosine annealing learning rate schedule starting at 10 3 and weight decay 10 2 for 250 epochs to convergence. In order to facilitate the learning, we set the number of edges to Ne = 128 and faces to Nf = 128. |