Zhao et al., 2022 - Google Patents
JSNet++: Dynamic filters and pointwise correlation for 3D point cloud instance and semantic segmentationZhao et al., 2022
- Document ID
- 12517211284871759253
- Author
- Zhao L
- Tao W
- Publication year
- Publication venue
- IEEE Transactions on Circuits and Systems for Video Technology
External Links
Snippet
In this paper, we propose a novel joint instance and semantic segmentation approach, called JSNet++, to address the instance and semantic segmentation tasks of 3D point clouds simultaneously. We first introduce a basic joint segmentation framework (JSNet). It fuses …
- 230000011218 segmentation 0 title abstract description 187
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/001—Model-based coding, e.g. wire frame
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhao et al. | JSNet++: Dynamic filters and pointwise correlation for 3D point cloud instance and semantic segmentation | |
Gu et al. | A review on 2D instance segmentation based on deep neural networks | |
Laga et al. | A survey on deep learning techniques for stereo-based depth estimation | |
Elasri et al. | Image generation: A review | |
He et al. | Deep learning based 3D segmentation: A survey | |
Qiu et al. | Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion | |
Zhang et al. | A review of deep learning-based semantic segmentation for point cloud | |
Ma et al. | Global context reasoning for semantic segmentation of 3D point clouds | |
Yang et al. | Lego: Learning edge with geometry all at once by watching videos | |
Liang et al. | 3D instance embedding learning with a structure-aware loss function for point cloud segmentation | |
Li et al. | Multi-scale neighborhood feature extraction and aggregation for point cloud segmentation | |
Ji et al. | Semi-supervised adversarial monocular depth estimation | |
Su et al. | DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds | |
Li et al. | Joint semantic-instance segmentation method for intelligent transportation system | |
Wang et al. | Densely connected graph convolutional network for joint semantic and instance segmentation of indoor point clouds | |
CN110853039A (en) | Multi-data fusion sketch image segmentation method, system, device and storage medium | |
He et al. | Learning scene dynamics from point cloud sequences | |
Cao et al. | Skeleton-based action recognition with temporal action graph and temporal adaptive graph convolution structure | |
Lee et al. | Connectivity-based convolutional neural network for classifying point clouds | |
Zheng et al. | PointRas: Uncertainty-aware multi-resolution learning for point cloud segmentation | |
Zhang et al. | Exploring semantic information extraction from different data forms in 3D point cloud semantic segmentation | |
Sarker et al. | A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation | |
Zhou et al. | GAF-Net: Geometric Contextual Feature Aggregation and Adaptive Fusion for Large-Scale Point Cloud Semantic Segmentation | |
Peng et al. | Mwformer: mesh understanding with window-based transformer | |
Sun et al. | A review of point cloud segmentation for understanding 3D indoor scenes |