Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
LION: Latent Point Diffusion Models for 3D Shape Generation
Authors: xiaohui zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 26,709 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,190 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, LION achieves state-of-the-art generation performance on multiple Shape Net benchmarks. |
| Researcher Affiliation | Collaboration | 1NVIDIA 2University of Toronto 3Vector Institute |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We will release code and instructions to reproduce all experiments upon acceptance of the manuscript. The internal guidelines of our institution prevent us from releasing code at this stage. |
| Open Datasets | Yes | To compare LION against existing methods, we use Shape Net [104], the most widely used dataset to benchmark 3D shape generative models. |
| Dataset Splits | Yes | Following previous works [31, 46], we train on three categories: airplane, chair, car. Also like previous methods, we primarily rely on Point Flow s [31] dataset splits and preprocssing. |
| Hardware Specification | Yes | All experiments are performed on NVIDIA DGX servers with NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions several software tools and libraries used (e.g., PyTorch, Mit Suba renderer), but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Our LION models use a batch size of 256 for all experiments. The encoder and decoder were trained with a learning rate of 1e-4 for 100 epochs, while the latent DDMs were trained with a learning rate of 2e-4 for 500 epochs. We use Adam optimizer... |