Xie, Y., Tian, J., & Zhu, X. (2020a). Linking points with labels in 3D: A review of point cloud semantic segmentation.Geoscience and Remote Sensing Magazine,8, 38–59. Xie, Y., Tian, J., & Zhu, X. X. (2020b). Linking points with labels in 3d: A review of point cloud s...
[CVPR2018] Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. [ECCV2018] 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation. [CVPR2019] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-...
* 题目: Multi-center anatomical segmentation with heterogeneous labels via landmark-based models* PDF: arxiv.org/abs/2211.0739* 作者: Nicolás Gaggion,Maria Vakalopoulou,Diego H. Milone,Enzo Ferrante 检测-域适应 1篇 * 题目: DATa: Domain Adaptation-Aided Deep Table Detection Using Visual-Lexical...
ScanComplete: large-scale scene completion and semantic segmentation for 3D scans. In: Proceedings of the IEEE conference on computer vision and pattern recognition CVPR; 2018. p. 4578–87. Sinha A, Unmesh A, Huang Q, Ramani K. SurfNet: generating 3D shape surfaces using deep residual ...
* 题目: RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation* PDF: arxiv.org/abs/2309.1047* 作者: Chang Liu,Giulia Rizzoli,Francesco Barbato,Umberto Michieli,Yi Niu,Pietro Zanuttigh* 题目: An Empirical Study of Attention Networks for Semantic Segmentation* PDF: ...
Instance-level semantic segmentations are provided for region (living room, kitchen) and object (sofa, TV) categories. SUNCG: A Large 3D Model Repository for Indoor Scenes (2017) [Link] The dataset contains over 45K different scenes with manually created realistic room and furniture layouts. All...
ScanComplete is a data-driven approach which takes an incomplete 3D scan of a scene as input and predicts a complete 3D model, along with per-voxel semantic labels. This work is based on our CVPR'18 paper,ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans. ...
用于定量评估的数据集有两个,分别是ScanNet和7-Scenes数据集。作者使用的是ScanNet数据集的两个训练/验证分割,和7-Scenes数据集的验证集。该方法的代码已经开源,可以在项目页https://zju3dv.github.io/neuralrecon/上找到。 以上信息主要来源于第7,5,3页 ...
* 题目: Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation* PDF: arxiv.org/abs/2312.0722* 作者: Yuanbin Wang,Shaofei Huang,Yulu Gao,Zhen Wang,Rui Wang,Kehua Sheng,Bo Zhang,Si Liu* 题目: Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly...
* 题目: LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention * PDF:https://arxiv.org/abs/2301.04275 * 作者: Ben Ding * 相关:https://github.com/fengluodb/LENet 三维视觉-点云处理 2篇 * [推荐]题目: AdaPoinTr: Diverse Point Cloud Completion wi...