DiffComplete: Diffusion-based Generative 3D Shape Completion
Authors: Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Niessner, Chi-Wing Fu, Jiaya Jia
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment4.1 Experimental Setup4.2 Main ResultsTable 1: Quantitative shape completion results on objects of known categoriesWe evaluate on two large-scale shape completion benchmarks: 3D-EPN [14] and Patch Complete [15]. |
| Researcher Affiliation | Collaboration | Ruihang Chu1 Enze Xie 2 Shentong Mo3 Zhenguo Li2 Matthias Nießner4 Chi-Wing Fu1 Jiaya Jia1,5 1The Chinese University of Hong Kong 2Huawei Noah s Ark Lab 3MBZUAI 4Technical University of Munich 5Smart More |
| Pseudocode | No | The paper describes the proposed method in detail and illustrates its architecture in Figure 2, but it does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The abstract provides a project website link 'https://ruihangchu.com/diffcomplete.html', but the paper text does not contain an explicit statement about releasing the source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate on two large-scale shape completion benchmarks: 3D-EPN [14] and Patch Complete [15]. ... It includes both the synthetic data from Shape Net [73] and the challenging real data from Scan Net [75]. |
| Dataset Splits | Yes | For a fair comparison, we follow their data splits and evaluation metrics, i.e., mean l1 error on the TUDF predictions across all voxels on 3D-EPN, and l1 Chamfer Distance (CD) and Intersection over Union (Io U) between the predicted and ground-truth shapes on Patch Complete. |
| Hardware Specification | Yes | We first train our network using a single partial scan as input by 200k iterations on four RTX3090 GPUs, taking around two days. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [76]' and the use of GPUs, implying certain software like PyTorch or TensorFlow, but does not provide specific version numbers for any libraries, frameworks, or operating systems used in the experiments. |
| Experiment Setup | Yes | Implementation details. We first train our network using a single partial scan as input by 200k iterations on four RTX3090 GPUs, taking around two days. If multiple conditions are needed, we finetune project layers ψ for additional 50k iterations. Adam optimizer [76] is employed with a learning rate of 1e 4 and the batch size is 32. |