Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Memory-Augmented Re-Completion for 3D Semantic Scene Completion
Authors: Yu-Wen Tseng, Sheng-Ping Yang, Jhih-Ciang Wu, I-Bin Liao, Yung-Hui Li, Hong-Han Shuai, Wen-Huang Cheng
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,167 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,261 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 | Extensive experiments conducted on the SSCBench-KITTI-360 and Semantic KITTI datasets validate the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | 1 National Taiwan University, Taiwan 2 National Yang Ming Chiao Tung University, Taiwan 3 National Taiwan Normal University, Taiwan 4 Hon Hai Research Institute, Taiwan |
| Pseudocode | Yes | Algorithm 1: Memory Updating |
| Open Source Code | Yes | Code https://github.com/ywtseng0226/MARE |
| Open Datasets | Yes | The evaluation is performed on SSCBench-KITTI-360 (Li et al. 2024) and Semantic KITTI (Behley et al. 2019) datasets |
| Dataset Splits | Yes | SSCBench-KITTI-360 provides 9 video sequences, with 7 for training, 1 for validation, and 1 for testing. Semantic KITTI provides 20 video sequences, with 9 for training, 1 for validation, and 11 for testing. |
| Hardware Specification | Yes | trained in an end-to-end manner with two NVIDIA V100 GPUs for 30 epochs |
| Software Dependencies | No | The paper mentions optimizers (Adam W), backbones (Res Net-50), and encoders (Mask DINO) but does not provide specific version numbers for software libraries or programming languages like Python or PyTorch. |
| Experiment Setup | Yes | The model is trained in an end-to-end manner with two NVIDIA V100 GPUs for 30 epochs, with a batch size of two images. We employ the Adam W as the optimizer, Res Net-50 as the backbone, and the pretrained weights of Mask DINO as the tokenbased Encoder. In the Regional Memory Bank, we set the size |B| as 1024 and the number of neighbor tokens k as 3. In the Re-completion pipeline, we re-complete the scene for two iterations. |