MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images
Authors: Eunji Hong, Minh Hieu Nguyen, Mikaela Angelina Uy, Minhyuk Sung
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments, comparing our method with previous work that takes a raw 3D point cloud as input, demonstrate the effectiveness of our approach by taking advantage of multi-view images. [...] Tab. 1 shows the quantitative results of CAD reconstruction on the Fusion360 [64] and Deep CAD [65] datasets. We see that MV2Cyl outperforms the baselines by considerable margins across both datasets in all evaluated metrics. |
| Researcher Affiliation | Collaboration | Eunji Hong 1, Minh Hieu Nguyen1, Mikaela Angelina Uy2,3, Minhyuk Sung1 1Korea Advanced Institute of Science and Technology, 2Stanford University, 3NVIDIA |
| Pseudocode | No | The paper describes its method through text and figures (e.g., Figure 1, Figure 4 showing pipelines and steps), but it does not contain any structured pseudocode or algorithm blocks with numbered or bulleted code-like steps explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper states 'Our project page can be found at https://mv2cyl.github.io.' However, in the NeurIPS Paper Checklist, the authors state for question '5. Open access to data and code': 'Answer: [No] Justification: The code and data will be made available upon the acceptance of this work.' This indicates that the code is not currently openly accessible. |
| Open Datasets | Yes | Datasets. We evaluate our approach on two sketch-extrude CAD datasets Fusion360 [64] and Deep CAD [65]. We use the train and test splits as released in [58]. |
| Dataset Splits | Yes | The datasets are divided into training and validation sets in a 9:1 ratio for the U-Net [50] training. |
| Hardware Specification | Yes | The training times for 3 epochs are 316 minutes for the Fusion360 [64] dataset and 381 minutes for the Deep CAD dataset, using a single NVIDIA RTX A6000 GPU. [...] We trained all the fields for 1500 iterations on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Blender Proc [7]' and 'Sci Py library [59]' but does not provide specific version numbers for these software components or any other key libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | We fix K = 8 instances following [58] and train with image resolutions of H = W = 400. [...] The training objective is defined as: ext{L}_{ ext{2D} ext{curve}}= ext{lambda}_{ ext{CE}} ext{L}_{ ext{cross-entropy}} + ext{lambda}_{ ext{focal}} ext{L}_{ ext{focal}} + ext{lambda}_{ ext{dice}} ext{L}_{ ext{dice}}, where ext{lambda}_{ ext{CE}}, ext{lambda}_{ ext{focal}}, and ext{lambda}_{ ext{dice}} are all set to 1.0... The opacity field ext{sigma}( ext{x}) hyper-parameters are set to ext{zeta}=10 and ext{beta}=0.8 as in [68]. For the reconstruction loss of the density field ext{F}, we set ext{lambda}_{ ext{sparsity}}=0.5, s = 0.5, ext{eta}_{ ext{batch}}=0.1, and ext{eta}_{ ext{image}}=0.05. We use a batch size of 8,192 rays for the training of both the density field ext{F} and the semantic field ext{A}. We trained all the fields for 1500 iterations... |