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..
Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes
Authors: Jie Liu, Pan Zhou, Zehao Xiao, Jiayi Shen, Wenzhe Yin, Jan-Jakob Sonke, Efstratios Gavves
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four 3D point cloud datasets demonstrate that NPISeg3D achieves superior segmentation performance with fewer clicks while providing reliable uncertainty estimations. Project Page: https://jliu4ai. github.io/NPISeg3D_projectpage/. |
| Researcher Affiliation | Academia | 1University of Amsterdam, Amsterdam, The Netherlands 2Singapore Management University, Singapore 3Netherlands Cancer Institute, Amsterdam, The Netherlands. |
| Pseudocode | No | The paper describes the methodology in detail but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Project Page: https://jliu4ai. github.io/NPISeg3D_projectpage/. ... we will release our code, along with the improved annotation tool, to facilitate future research. |
| Open Datasets | Yes | We follow the dataset setup as in prior work (Yue et al., 2023) and train our model on the Scan Net V2-Train dataset (Dai et al., 2017). ... For evaluation, we consider two types of datasets: (1) In-domain dataset: Scan Net V2Val (Dai et al., 2017)... (2) Out-of-domain datasets: S3DIS (Armeni et al., 2016)... Replica (Straub et al., 2019)... and KITTI-360 (Liao et al., 2022)... Additionally, we further evaluate the part-level segmentation capability of our NPISeg3D and existing models on the Part Net dataset (Mo et al., 2019). |
| Dataset Splits | Yes | We follow the dataset setup as in prior work (Yue et al., 2023) and train our model on the Scan Net V2-Train dataset (Dai et al., 2017). For evaluation, we consider two types of datasets: (1) In-domain dataset: Scan Net V2-Val (Dai et al., 2017)... |
| Hardware Specification | Yes | Training is performed on a single Tesla A6000 GPU with a batch size of 5. |
| Software Dependencies | No | The paper mentions using Minkowski Res16UNet34C (Choy et al., 2019) backbone and Adam optimizer, but does not specify version numbers for any software dependencies like Python, PyTorch, CUDA, etc. |
| Experiment Setup | Yes | We train NPISeg3D end-to-end for 600 epochs using the Adam optimizer with an initial learning rate of 0.0005. The learning rate is reduced by a factor of 0.1 after 500 epochs to facilitate convergence. Training is performed on a single Tesla A6000 GPU with a batch size of 5. The KL loss coefficient Ξ»klin Eq. (10) is set to 0.005. For the segmentation loss Lseg, we use a combination of cross-entropy loss and dice loss, with coefficients of 1 and 2, respectively. |