Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Feature Matching in Implicit 3D Representations
Authors: Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves
ICML 2021 | Venue PDF | 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. |