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..
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
| 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... |