NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Authors: Stefan Lionar, Xiangyu Xu, Min Lin, Gim Hee Lee
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that NU-MCC is able to learn a strong 3D representation, significantly advancing the state of the art in single-view 3D reconstruction. Particularly, it outperforms MCC by 9.7% in terms of the F1-score on the CO3D-v2 dataset with more than 5 faster running speed. |
| Researcher Affiliation | Collaboration | Stefan Lionar1,2 Xiangyu Xu3B Min Lin1 Gim Hee Lee2 1Sea AI Lab 2National University of Singapore 3Xi an Jiaotong University |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page: https://numcc.github.io/ |
| Open Datasets | Yes | We conduct extensive experiments to show the representational power and generalization capability of NU-MCC for object-level single-view reconstruction using CO3D-v2 dataset [5]. |
| Dataset Splits | Yes | We use the training-validation split from MCC all categories experiment. The quantitative results on CO3D-v2 [5] validation set are summarized in Table 1. |
| Hardware Specification | Yes | Our model is trained with an effective batch size of 512 using 4 NVIDIA A100 GPUs for 100 epochs. |
| Software Dependencies | No | The paper mentions using "Adam optimizer [47]" but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Our model is trained with an effective batch size of 512 using 4 NVIDIA A100 GPUs for 100 epochs. One epoch takes approximately 2 hours. We follow the optimizer and 3D data augmentation of MCC. Adam optimizer [47] with base learning rate of 10 4, cosine schedule, and linear warm-up for the first 5% of iterations are used. 3D data augmentation is performed by random scaling of s [0.8, 1.2] and rotation θ [ 180 , 180 ] along each axis. |