Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation
Authors: Yujun Chen, Xin Tan, Zhizhong Zhang, Yanyun Qu, Yuan Xie
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The ablation of the IPSL module demonstrates its robust adaptability, and the experiments evaluated on Semantic KITTI and nu Scenes demonstrate that our model outperforms the state-of-the-art method, Laser Mix. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, East China Normal University 2Chongqing Institute, East China Normal University 3School of Information Science and Engineering, Xiamen University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Semantic KITTI (Behley, Milioto, and Stachniss 2021) contains 10 (1/11) training (validation/testing) sequences and totally 43551 Li DAR scans with a 64-beam Li DAR sensor, as well as binocular camera images of each scan additionally. There are point-wise panoptic annotations with 8 thing class and 12 stuff class labels with instance labels. nu Scenes (Caesar et al. 2020) is a large-scale autopilot dataset with various urban scenes and a 32-beam Li DAR sensor. |
| Dataset Splits | Yes | We adopt a sampling method of selecting frames with fixed intervals, which is the same as Kong et al. (2023). In this paper, we have respectively selected 40%, 20%, 10%, and 1% point cloud frames with point-wise labels for training. Table 1: Segmentation results compared with other semi-supervised methods on validation set of nu Scenes and Semantic KITTI. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like MMdetection, Swift Net, Cylinder3D, Panoptic Polar Net, SAM, and Grounded-Segment-Anything, but does not specify their version numbers. |
| Experiment Setup | Yes | Implementation Default settings unless ablation. In IPSL Module, the radius of Gaussian heatmaps R = 5, percentage of scale to the center Pcenter = 1/4. For Cylinder Mix, the region size [Rx, Ry, Rz] = [4, 4, 2], the probability pclymix = 25%. For the weight of each term in loss in Lseg, µhm = 100, µos = 10, µfm = 1. |