VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion
Authors: Meng Wang, Huilong Pi, Ruihui Li, Yunchuan Qin, Zhuo Tang, Kenli Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that VLScene achieves rank-1st performance on challenging benchmarks Semantic KITTI and SSCBench KITTI-360, yielding remarkably m Io U scores of 17.52 and 19.10, respectively. To evaluate the performance of VLScene, we conduct thorough experiments using the large outdoor datasets Semantic KITTI (Behley et al. 2019) and SSCBench-KITTI360 (Li et al. 2023b). |
| Researcher Affiliation | Academia | College of Computer Science and Electronic Engineering, Hunan University, Changsha, China EMAIL |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations, but it does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/willemeng/VLScene |
| Open Datasets | Yes | To evaluate the performance of VLScene, we conduct thorough experiments using the large outdoor datasets Semantic KITTI (Behley et al. 2019) and SSCBench-KITTI360 (Liao, Xie, and Geiger 2022; Li et al. 2023b). |
| Dataset Splits | Yes | Quantitative Results Table 1 presents a comparison of our VLScene with other state-of-the-art camera-based SSC methods on the Semantic KITTI hidden test set. As shown in Table 2, VLScene also exhibits a significant advantage in semantic and geometric analysis over current camera-based approaches on the rich data samples SSCBench-KITTI-360 benchmark. Furthermore, Table 3 shows that we provide different ranges of results on the Semantic KITTI validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the training loss function and balancing coefficients (L = λssc Lssc + λkd Lkd), but does not provide specific values for hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings. It states 'where several λ are balancing coefficients' without giving their concrete values. |