NeuForm: Adaptive Overfitting for Neural Shape Editing
Authors: Connor Lin, Niloy Mitra, Gordon Wetzstein, Leonidas J. Guibas, Paul Guerrero
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate NEUFORM on multiple applications: (i) reconstruction (i.e., projecting a given input to an adaptive overfitted latent space); (ii) part based shape editing; and (iii) shape mixing (i.e., converting an arrangement of parts taken from different models into a coherent shape model). We compare with two state-of-the-art approaches [16, 39] and demonstrate advantages, both quantitatively and qualitatively. |
| Researcher Affiliation | Collaboration | Connor Z. Lin Stanford University Niloy J. Mitra Adobe / UCL Gordon Wetzstein Stanford University Leonidas Guibas Stanford University Paul Guerrero Adobe |
| Pseudocode | No | The paper describes methods using mathematical equations and textual explanations, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | If accepted, we plan to release code, including instruction how to reproduce the results, one or two months after the notification. |
| Open Datasets | Yes | Dataset. We use the Part Net [25] dataset for our experiments.[25] Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su. Part Net: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. |
| Dataset Splits | No | The paper states 'training/test split of 6000/1800, 2100/400, and 3500/500 for chairs, lamps, and tables, respectively,' but does not explicitly provide details for a separate validation split. |
| Hardware Specification | Yes | Training the generalizable model takes roughly 33 hours on a Titan Xp GPU and training the overfitted model takes roughly 25 minutes on a single V100 GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but does not provide specific version numbers for software libraries or dependencies like PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | Training details. We train the generalizable model for 1000 epochs using the Adam [17] optimizer with a learning rate of 1e 4 and an exponential learning rate decay of 0.994 per epoch. In each epoch, we train on 4096 query points per shape with a batchsize of 1 shape. We sample 12.5% of the points uniformly in the [ 1, 1] cube and 87.5% of the points around the surface with a Guassian offset (N(0, 0.05)). The overfitted model is trained for 100 epochs on a single shape using the same training setup. |