Neural Feature Matching in Implicit 3D Representations
Authors: Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments and Evaluations |
| Researcher Affiliation | Collaboration | 1Informatics Institute, University of Amsterdam, the Netherlands 2AI3, IHPC, A*STAR, Singapore 3School of Informatics, University of Edinburgh, Scotland 4Google Research, Amsterdam, the Netherlands. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We use the improved implementation from the authors at https://github.com/czq142857/IM-NET-pytorch, which has some subtle differences from the original paper. |
| Open Datasets | Yes | We use objects from the Shape Net dataset (Chang et al., 2015). |
| Dataset Splits | No | The paper mentions using a 'test split' of Shape Net Part for evaluation, but does not specify a train/validation/test split for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'IM-Net (Chen & Zhang, 2019)' and 'Leaky-ReLU activations' but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | The optimisation uses the following settings: we use t = 0.02 for a total of 50 intermediate steps with latent code interpolation. For the number of Newton s iterations at each time step we use N = 3. The regularisation factor λ is set as 0.01. |