Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Cascade Generation on Point Sets
Authors: Kaiqi Wang, Ke Chen, Kui Jia
IJCAI 2019 | Venue PDF | 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. |