Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Authors: Shoulong Zhang, shuai li, Aimin Hao, Hong Qin
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments confirm that, our method can successfully learn representative knowledge embedding, and the obtained prior knowledge can effectively enhance the accuracy of relationship predictions. |
| Researcher Affiliation | Academia | Shoulong Zhang Beihang University shoulong.zhang@buaa.edu.cn Shuai Li Beihang University Peng Cheng Laboratory lishuai@buaa.edu.cn Aimin Hao Beihang University Peng Cheng Laboratory ham@buaa.edu.cn Hong Qin Stony Brook University (SUNY) qin@cs.stonybrook.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and instructions are included in the supplemental material. |
| Open Datasets | Yes | We train the meta-learning auto-encoder and the scene graph prediction model on the 3DSSG dataset [29] 1, a 3D scene graph dataset based on 3RScan [28]. |
| Dataset Splits | Yes | With the same sub-scene split in [29], there are 3582 scenes in the training set and 548 for evaluation. |
| Hardware Specification | Yes | Our model is implemented in Py Torch. We trained our model on an Nvidia RTX 2080Ti GPU in a personal computer platform for 40 epochs with the ADAM optimizer. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The initial learning rate is set to 0.0001, and the decay rate is 0.7 for every ten epochs. We followed the focal loss parameter settings in [29]. |