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..
UV-free Texture Generation with Denoising and Geodesic Heat Diffusion
Authors: Simone Foti, Stefanos Zafeiriou, Tolga Birdal
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two datasets, the chairs of Shape Net [9] and the Amazon Berkeley Objects (ABO) dataset [15]. As we can observe from the quantitative results reported in Tab. 1, our method significantly outperforms Point-UV Diffusion across all metrics. Ablation Studies. We here perform multiple ablations to examine how much each model component, conditioning, and choice contributes to the overall performance. |
| Researcher Affiliation | Academia | Simone Foti Stefanos Zafeiriou Tolga Birdal Department of Computing Imperial College London |
| Pseudocode | No | The paper describes its methods in detail through text and diagrams (e.g., Figure 3, Figure 4, Figure 9), but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our code and pre-trained models are available at github.com/simofoti/UV3-Te D. |
| Open Datasets | Yes | We conduct experiments on two datasets, the chairs of Shape Net [9] and the Amazon Berkeley Objects (ABO) dataset [15]. |
| Dataset Splits | Yes | In both cases, we operate a 90 : 5 : 5 split between train, test, and validation sets. |
| Hardware Specification | Yes | We run our models on a single Nvidia A100 with 40GB of dedicated memory. |
| Software Dependencies | No | The paper states, 'We implement our method using Py Torch [37], Pytorch Geometric [21] and Diffusers [52]', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use 32 sihks, K = 128 eigenvalues, and a mixed-LBO weighting of ϱ = 0.05. We train our models using the Adam W [30] optimiser for 400 epochs on chairs and 250 on ABO, with a learning rate of 1e 4 and a cosine annealing with 500 warmup iteration steps. We use T = 1, 000 DDPM timesteps, S = 250 farthest point samples in the attention layers, and P = 5, 000 target PDS samples. Our batch size is set to 8 on Shape Net chairs and to 6 on ABO. |