Deep Cascade Generation on Point Sets
Authors: Kaiqi Wang, Ke Chen, Kui Jia
IJCAI 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative evaluation on the publicly benchmarking Shape Net dataset demonstrates superior performance of the proposed model to the state-of-the-art methods on both single-view shape reconstruction and shape autoencoding applications. |
| Researcher Affiliation | Academia | Kaiqi Wang , Ke Chen and Kui Jia South China University of Technology EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Source codes of our DCG method are available1. 1https://wkqscut.github.io/DCGNet/. |
| Open Datasets | Yes | We conduct experiments on the popular Shape Net Core dataset (v2) [Chang et al., 2015], which has been widely adopted in 3D shape reconstruction [Choy et al., 2016; Fan et al., 2017; Groueix et al., 2018] and autoencoding [Yang et al., 2018]. |
| Dataset Splits | No | We follow the settings in [Choy et al., 2016; Groueix et al., 2018], i.e., 31746 models for training and the remaining 7943 for testing. No explicit mention of a validation split was found. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory amounts) used for running the experiments were provided. |
| Software Dependencies | No | The paper mentions software components like ResNet-18, PointNet, and the ADAM optimizer, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We used the ADAM to train the model for a total of 420 epochs with an initial learning rate of 0.001 and batch size 32. For step decay on the learning rate, it is dropped by a factor of 0.1 after 300 and 400 epochs. |