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 | Archive PDF | Plain Text | 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 λklin 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. |