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 [1].
Diverse Shape Completion via Style Modulated Generative Adversarial Networks
Authors: Wesley Khademi, Fuxin Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our method against a variety of baselines on the task of multimodal shape completion and show superior quantitative and qualitative results across several synthetic and real datasets. We further conduct a series of ablations to justify the design choices of our method. |
| Researcher Affiliation | Academia | Wesley Khademi Oregon State University EMAIL Li Fuxin Oregon State University EMAIL |
| Pseudocode | No | The paper describes the method using diagrams and textual descriptions, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of open-source code or a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on several synthetic and real datasets. Following the setup of [8], we evaluate our approach on the Chair, Table, and Airplane categories of the 3D-EPN dataset [58]. Similarly, we also perform experiments on the Chair, Table, and Lamp categories from the Part Net dataset [59]. To evaluate our method on real scanned data, we conduct experiments on the Google Scanned Objects (GSO) dataset [60]. |
| Dataset Splits | No | The paper mentions training, but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or predefined split citations) for reproduction. |
| Hardware Specification | Yes | All models are trained on two NVIDIA Tesla V100 GPUs and take about 30 hours to train. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, TensorFlow, specific libraries or solvers and their versions). |
| Experiment Setup | Yes | Implementation Details Our model takes in NP = 1024 points as partial input and produces N = 2048 points as a completion. For training the generator, the Adam optimizer is used with an initial learning rate of 1 10 4 and the learning rate is linearly decayed every 2 epochs with a decay rate of 0.98. For the discriminator, the Adam optimizer is used with a learning rate of 1 10 4. We train a separate model for each shape category and train each model for 300 epochs with a batch size of 56. |