Giang et al., 2020 - Google Patents
Sequential depth completion with confidence estimation for 3d model reconstructionGiang et al., 2020
View PDF- Document ID
- 10732766646945679578
- Author
- Giang K
- Song S
- Kim D
- Choi S
- Publication year
- Publication venue
- IEEE Robotics and Automation Letters
External Links
Snippet
This letter addresses a depth-completion problem for sequential data to reconstruct 3D models of outdoor scenes. While many deep-learning-based approaches have recently achieved promising results, their results are not directly applicable to 3D modeling because …
- 230000000875 corresponding 0 abstract description 10
Classifications
-
- 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
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- 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
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- 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
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/10—Geometric effects
- G06T15/20—Perspective computation
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hambarde et al. | UW-GAN: Single-image depth estimation and image enhancement for underwater images | |
Weder et al. | Routedfusion: Learning real-time depth map fusion | |
Song et al. | Robustness-aware 3d object detection in autonomous driving: A review and outlook | |
CN111445476A (en) | Monocular depth estimation method based on multimodal unsupervised image content decoupling | |
Rich et al. | 3dvnet: Multi-view depth prediction and volumetric refinement | |
Samavati et al. | Deep learning-based 3D reconstruction: a survey | |
Xie et al. | Recent advances in conventional and deep learning-based depth completion: A survey | |
Giang et al. | Sequential depth completion with confidence estimation for 3d model reconstruction | |
JP2022521253A (en) | Image processing to determine the thickness of an object | |
Hwang et al. | Lidar depth completion using color-embedded information via knowledge distillation | |
Song et al. | Prior depth-based multi-view stereo network for online 3D model reconstruction | |
Luginov et al. | Swiftdepth: An efficient hybrid cnn-transformer model for self-supervised monocular depth estimation on mobile devices | |
Jeon et al. | Struct-MDC: Mesh-refined unsupervised depth completion leveraging structural regularities from visual SLAM | |
CN117745944A (en) | Pre-training model determining method, device, equipment and storage medium | |
Yang et al. | Mixed-scale unet based on dense atrous pyramid for monocular depth estimation | |
Wang et al. | Depth estimation of supervised monocular images based on semantic segmentation | |
Xu et al. | Mrftrans: Multimodal representation fusion transformer for monocular 3d semantic scene completion | |
Luo et al. | FD-SLAM: a semantic SLAM based on enhanced fast-SCNN dynamic region detection and DeepFillv2-Driven background inpainting | |
Sohail et al. | Deep transfer learning for 3d point cloud understanding: A comprehensive survey | |
Dinh et al. | Feature engineering and deep learning for stereo matching under adverse driving conditions | |
Mondal et al. | Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation | |
CN113505834A (en) | Method for training detection model, determining image updating information and updating high-precision map | |
Dao et al. | FastMDE: A fast CNN architecture for monocular depth estimation at high resolution | |
Zhang et al. | Pmvc: Promoting multi-view consistency for 3d scene reconstruction | |
Xing et al. | Scale-consistent fusion: from heterogeneous local sampling to global immersive rendering |