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..
Unified 3D Segmenter As Prototypical Classifiers
Authors: Zheyun Qin, Cheng Han, Qifan Wang, Xiushan Nie, Yilong Yin, Lu Xiankai
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that PROTOSEG outperforms concurrent well-known specialized architectures on 3D point cloud benchmarks, achieving 72.3%, 76.4% and 74.2% m Io U for semantic segmentation on S3DIS, Scan Net V2 and Semantic KITTI, 66.8% m Cov and 51.2% m AP for instance segmentation on S3DIS and Scan Net V2, 62.4% PQ for panoptic segmentation on Semantic KITTI, validating the strength of our concept and the effectiveness of our algorithm. |
| Researcher Affiliation | Collaboration | Zheyun Qin1 , Cheng Han2 , Qifan Wang3, Nie Xiushan4, Yilong Yin1 , Xiankai Lu1 1Shandong University, 2Rochester Institute of Technology, 3Meta AI, 4Shandong Jianzhu University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and models are available at https://github.com/zyqin19/PROTOSEG. |
| Open Datasets | Yes | S3DIS [9], a large-scale indoor point cloud dataset, encompasses point clouds from 271 rooms across 6 areas." "Scan Net V2 [10] provides over 1,500 indoor scenes and around 2.5 million annotated RGB-D images..." "Semantic KITTI [11] is introduced based on the well-known KITTI Vision [71] benchmark... |
| Dataset Splits | Yes | Table 2: Comparisons of semantic segmentation with m Io U on Scan Net v2 [10] (see 5.1). Method Test Val. and Table 3: Comparisons of semantic segmentation performance on Semantic KITTI val set (see 5.1). |
| Hardware Specification | Yes | Training and testing are conducted on eight NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers, such as Python library versions or framework versions. |
| Experiment Setup | Yes | The hyper-parameter Îș balances the convergence speed and stability of Eq. 10 in addition to smoothing the association (Eq. 9). We just use Îș = 0.05 following [30] for our experiments, not extensively fine-tuned." and "Our model achieves the best performance when the momentum coefficient is set to 0.999." and "The m Io U score peaks at 72.34% when K = 10." |