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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Authors: Shoulong Zhang, shuai li, Aimin Hao, Hong Qin
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,316 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,628 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conο¬rm 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 EMAIL Shuai Li Beihang University Peng Cheng Laboratory EMAIL Aimin Hao Beihang University Peng Cheng Laboratory EMAIL Hong Qin Stony Brook University (SUNY) EMAIL |
| 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]. |