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 | Archive PDF | Plain Text | LLM Run Details
| 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. |