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 Star Domain as Primitive Representation
Authors: Yuki Kawana, Yusuke Mukuta, Tatsuya Harada
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the reconstruction performance of an NSD compared with state-of-the-art methods for an input RGB image. The quantitative results are shown in Table 2. In the experiments, we use the Shape Net [32] dataset. |
| Researcher Affiliation | Academia | Yuki Kawana1, Yusuke Mukuta1,2, Tatsuya Harada1,2 1The University of Tokyo, 2RIKEN AIP |
| Pseudocode | No | The paper describes its approach and architecture through text and diagrams (Figure 2) but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | In the experiments, we use the Shape Net [32] dataset. |
| Dataset Splits | Yes | In addition, we use the same samples and data split as in [25]. The threshold τo of the composite indicator function is determined by a grid search over the validation set. |
| Hardware Specification | Yes | All speed measurements are performed on an NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions using ResNet18 and Adam optimizer, but it does not provide specific version numbers for these or other software dependencies, such as PyTorch. |
| Experiment Setup | Yes | N is set to 30 by default, unless stated otherwise. We use Res Net18 as the encoder E... For the translation network T, we use a multilayer perceptron (MLP) with three hidden layers with (128, 128, N 3) units with Re LU activation. For an NSD, we use an MLP with three hidden layers with (64, 64, 1) units and Re LU activation. We set the margin α of the indicator function to 100. ... During training, we use a batch size of 20, and train with the Adam optimizer, with a learning rate of 0.0001. We set the weight of Lo and Ls as 1 and 10, respectively. |