Neural Star Domain as Primitive Representation
Authors: Yuki Kawana, Yusuke Mukuta, Tatsuya Harada
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |