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..
Context and Geometry Aware Voxel Transformer for Semantic Scene Completion
Authors: Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, Hui-liang Shen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that CGFormer achieves state-of-the-art performance on the Semantic KITTI and SSCBench-KITTI-360 benchmarks, attaining a m Io U of 16.87 and 20.05, as well as an Io U of 45.99 and 48.07, respectively. |
| Researcher Affiliation | Collaboration | Zhu Yu1 Runmin Zhang1 Jiacheng Ying1 Junchen Yu1 Xiaohai Hu3 Lun Luo4 Si-Yuan Cao2,1 Hui-Liang Shen1 1Zhejiang University 2Ningbo Innovation Center, Zhejiang University 3University of Washington 4HAOMO.AI Technology Co., Ltd. |
| Pseudocode | No | The paper describes the architecture and processes using diagrams and text, but it does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | https://github.com/pkqbajng/CGFormer |
| Open Datasets | Yes | We evaluate our CGFormer on two datasets: Semantic KITTI [1] and SSC-Bench-KITTI-360 [22]. |
| Dataset Splits | Yes | Semantic KITTI provides RGB images... The dataset includes 10 sequences for training, 1 sequence for validation, and 11 sequences for testing. SSC-Bench-KITTI-360 [22] offers 7 sequences for training, 1 sequence for validation, and 1 sequence for testing. |
| Hardware Specification | Yes | We train CGFormer for 25 epochs on 4 NVIDIA 4090 GPUs, with a batch size of 4. It approximately consumes 19 GB of GPU memory on each GPU during the training phase. |
| Software Dependencies | No | Consistent with previous researches [13, 3, 47], we utilize a 2D UNet based on a pretrained Efficient Net B7 [41] as the image backbone. ... Swin T [30] is employed as the 2D backbone in the TPV-based branch. |
| Experiment Setup | Yes | We train CGFormer for 25 epochs on 4 NVIDIA 4090 GPUs, with a batch size of 4. It approximately consumes 19 GB of GPU memory on each GPU during the training phase. We employ the Adam W [32] optimizer with β1 = 0.9, β2 = 0.99 and set the maximum learning rate to 3 × 10−4. The cosine annealing learning rate strategy is adopted for the learning rate decay, where the cosine warmup strategy is applied for the first 5% iterations. |