Li et al., 2021 - Google Patents
D2im-net: Learning detail disentangled implicit fields from single imagesLi et al., 2021
View PDF- Document ID
- 5835481600620268542
- Author
- Li M
- Zhang H
- Publication year
- Publication venue
- Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
External Links
Snippet
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction …
- 238000006073 displacement reaction 0 abstract description 44
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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/10—Geometric effects
- G06T15/20—Perspective computation
- G06T15/205—Image-based rendering
-
- 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
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- G06T2207/30—Subject of image; Context of image processing
-
- 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
- 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
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/50—Lighting effects
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
-
- 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
- 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 |
---|---|---|
Li et al. | D2im-net: Learning detail disentangled implicit fields from single images | |
Alldieck et al. | Photorealistic monocular 3d reconstruction of humans wearing clothing | |
Wang et al. | Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction | |
Hou et al. | 3d-sis: 3d semantic instance segmentation of rgb-d scans | |
Wang et al. | Flownet3d++: Geometric losses for deep scene flow estimation | |
Wang et al. | Adaptive O-CNN: A patch-based deep representation of 3D shapes | |
Liu et al. | Robust dynamic radiance fields | |
Kulon et al. | Weakly-supervised mesh-convolutional hand reconstruction in the wild | |
Baek et al. | Pushing the envelope for rgb-based dense 3d hand pose estimation via neural rendering | |
Xie et al. | Fig-nerf: Figure-ground neural radiance fields for 3d object category modelling | |
Lin et al. | Photometric mesh optimization for video-aligned 3d object reconstruction | |
Dupont et al. | Equivariant neural rendering | |
Kar et al. | Learning a multi-view stereo machine | |
Yao et al. | Front2back: Single view 3d shape reconstruction via front to back prediction | |
Raj et al. | Pva: Pixel-aligned volumetric avatars | |
Ji et al. | SurfaceNet+: An end-to-end 3D neural network for very sparse multi-view stereopsis | |
Raj et al. | Pixel-aligned volumetric avatars | |
Pavllo et al. | Shape, pose, and appearance from a single image via bootstrapped radiance field inversion | |
Hu et al. | Hvtr: Hybrid volumetric-textural rendering for human avatars | |
Liu et al. | High-quality textured 3D shape reconstruction with cascaded fully convolutional networks | |
Sun et al. | Ssl-net: Point-cloud generation network with self-supervised learning | |
Khan et al. | An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data | |
Lu et al. | Single image shape-from-silhouettes | |
Hani et al. | Continuous object representation networks: Novel view synthesis without target view supervision | |
Zeng et al. | Inferring point clouds from single monocular images by depth intermediation |