Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
Authors: Biao Zhang, Peter Wonka
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model using the problem of 3d shape reconstruction from a single image and improve upon the state of the art. We compare our method with a list of state-of-the-art methods quantitatively in Table 2. We improve the most important metric, F-score, from 51.75% to 59.66% compared to the previous state of the art Occ Net (Mescheder et al., 2019). We also improve upon Occ Net in the two other metrics. The experiments are evaluated on a single image 3D reconstruction dataset and improve over the SOTA. |
| Researcher Affiliation | Academia | Biao Zhang & Peter Wonka KAUST {biao.zhang, peter.wonka}@kaust.edu.sa |
| Pseudocode | Yes | We describe the detailed training process in Algorithm 1. This algorithm can be viewed along with Fig. 2. We also show the full process how we generate a triangular mesh given an input image in Algorithm 2. |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We perform single image 3d reconstruction on the Shape Net (Chang et al., 2015) dataset. The rendered RGB images and data split are taken from (Choy et al., 2016). |
| Dataset Splits | Yes | The rendered RGB images and data split are taken from (Choy et al., 2016). At training time, 1024 points are drawn from the bounding box and 1024 nearsurface . This is the sampling strategy proposed by Cvx Net. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used to run the experiments. It only mentions general computing operations without hardware details. |
| Software Dependencies | No | The paper mentions software components like 'Efficient Net B1' and 'Adam' but does not provide specific version numbers for these or other libraries/frameworks, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We use λ = 0.005 for ridge regression and C = 1 for SVM in all experiments. The training batch size is 32. We use Adam (Kingma & Ba, 2014) with learning rate 2e 4 as our optimizer. The learning rate is decayed with a factor of 0.1 after 500 epochs. |