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..
LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
Authors: Biao Zhang, Peter Wonka
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The main autoencoding experiment is trained on Objaverse (Deitke et al., 2023). Models are zero-centered and normalized into the unit sphere. [...] We use Chamfer distance and F-score as the metrics. The results are shown in Table 4. [...] To further prove the generalization ability of La Ge M-Objaverse, we also test the autoencoding on various datasets, including Thingi10k (Zhou & Jacobson, 2016), ABO (Collins et al., 2022), EGAD (Morrison et al., 2020), GSO (Downs et al., 2022), pix3d (Sun et al., 2018) and FAUST (Bogo et al., 2014). |
| Researcher Affiliation | Academia | Biao Zhang, Peter Wonka KAUST, Saudi Arabia EMAIL |
| Pseudocode | Yes | A.1 VOLUME POINTS SAMPLING. We sample volume points uniformly in the bounding sphere. 1 N_vol = 250000 2 vol_points = np.random.randn(N_vol, 3) 3 vol_points = vol_points / np.linalg.norm(vol_points, axis=1)[:, None] * |
| Open Source Code | Yes | We release our model trained on a 600k geometry dataset. |
| Open Datasets | Yes | The main autoencoding experiment is trained on Objaverse (Deitke et al., 2023). [...] To further prove the generalization ability of La Ge M-Objaverse, we also test the autoencoding on various datasets, including Thingi10k (Zhou & Jacobson, 2016), ABO (Collins et al., 2022), EGAD (Morrison et al., 2020), GSO (Downs et al., 2022), pix3d (Sun et al., 2018) and FAUST (Bogo et al., 2014). |
| Dataset Splits | Yes | We also apply the method to Shape Net, where the train split is taken from (Zhang et al., 2022). [...] Table 5: Generalization on Various Datasets. [...] Shape Net (Chang et al., 2015)-test 2k Yes 3.25 2.33 -0.92 97.41 99.49 2.08 |
| Hardware Specification | Yes | For Shape Net, the denoising networks of the 3 levels have 12 self-attention blocks with 768 channels. We trained the model for around 200 hours with 4 A100 GPUs. [...] The model is trained on 16 A100 GPUs for around 100 hours. |
| Software Dependencies | No | The proposed regularization (see Table 2) is implemented with layer normalization (Py Torch code). |
| Experiment Setup | Yes | The three levels of latents are 128 x 64, 512 x 32, and 2048 x 16 (where 64, 32, and 16 are channels of the latents). Some other hyperparameters of the network can also be found in Table 3. [...] For Shape Net, the denoising networks of the 3 levels have 12 self-attention blocks with 768 channels. [...] The loss is binary cross entropy as in previous work (Zhang et al., 2022). |