Zhao et al., 2017 - Google Patents
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognitionZhao et al., 2017
View PDF- Document ID
- 11085970717758527172
- Author
- Zhao C
- Sun L
- Stolkin R
- Publication year
- Publication venue
- 2017 18th international conference on advanced robotics (ICAR)
External Links
Snippet
This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, eg robotic interventions in nuclear …
- 239000000463 material 0 title abstract description 76
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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06T2207/20112—Image segmentation details
-
- 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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- 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
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhao et al. | A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition | |
Labbé et al. | Megapose: 6d pose estimation of novel objects via render & compare | |
Xiang et al. | Learning rgb-d feature embeddings for unseen object instance segmentation | |
Xie et al. | Unseen object instance segmentation for robotic environments | |
Li et al. | Multiple-human parsing in the wild | |
Wang et al. | Sgpn: Similarity group proposal network for 3d point cloud instance segmentation | |
Schwarz et al. | RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features | |
Torresani et al. | A dual decomposition approach to feature correspondence | |
Bideau et al. | It’s moving! a probabilistic model for causal motion segmentation in moving camera videos | |
Dwibedi et al. | Deep cuboid detection: Beyond 2d bounding boxes | |
Mishra et al. | Active segmentation for robotics | |
De Bem et al. | Deep fully-connected part-based models for human pose estimation | |
Zhao et al. | Semantic mapping for object category and structural class | |
Yasir et al. | 3D instance segmentation using deep learning on RGB-D indoor data | |
Nanwani et al. | Instance-level semantic maps for vision language navigation | |
Ye et al. | Stedge: self-training edge detection with multilayer teaching and regularization | |
Tang et al. | Probabilistic object tracking with dynamic attributed relational feature graph | |
Duong et al. | Accurate sparse feature regression forest learning for real-time camera relocalization | |
Herbst et al. | Object segmentation from motion with dense feature matching | |
Koppula et al. | Human activity learning using object affordances from rgb-d videos | |
Li et al. | Recent advances on application of deep learning for recovering object pose | |
Pohlen et al. | Semantic segmentation of modular furniture | |
Wang et al. | An approach for construct semantic map with scene classification and object semantic segmentation | |
Mason et al. | Unsupervised discovery of object classes with a mobile robot | |
Ghafarianzadeh et al. | Efficient, dense, object-based segmentation from RGBD video |