Deep Point Cloud Reconstruction
Authors: Jaesung Choe, ByeongIn Joung, Francois Rameau, Jaesik Park, In So Kweon
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our network achieves state-of-the-art performance among the recent studies in the Scan Net, ICL-NUIM, and Shape Net Part datasets. |
| Researcher Affiliation | Academia | KAIST1, POSTECH2 |
| Pseudocode | No | The paper does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not explicitly state that open-source code for the described methodology is provided, nor does it include a link to a code repository. |
| Open Datasets | Yes | Our point reconstruction network has been solely trained on the Shape Net-Part (Yi et al., 2016) dataset but tested on other real and synthetic datasets such as Scan Net (Dai et al., 2017) and ICL-NUIM (Handa et al., 2014). |
| Dataset Splits | Yes | To train and validate the networks, we carefully follow the official train/val/test split provided by (Yi et al., 2016). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We first train our voxel generation network for 10 epochs using the Adam optimizer (Kingma & Ba, 2014) with initial learning of 1e-3 and a batch size of 4. We decrease the learning rate by half for every 2 epochs. [...] For the sake of fairness, we adjust the unit voxel length as lvox=0.0200 for training the networks. [...] We follow a data augmentation scheme proposed by the previous point upsampling study (Li et al., 2021), such as random noise addition and random re-scaling. Additionally, we include random outliers that constitute less that 5 percent of input points. |