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  • Produktbild: Computer Vision – ECCV 2022
  • Produktbild: Computer Vision – ECCV 2022
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Computer Vision – ECCV 2022 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part I

Aus der Reihe Lecture Notes in Computer Science
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104,99 € UVP 120,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2022

Abbildungen

LVI, 747 p. 269 illus., 265 illus. in color.

Herausgeber

Shai Avidan + weitere

Verlag

Springer

Seitenzahl

747

Maße (L/B/H)

23,5/15,5/4,3 cm

Gewicht

1194 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-031-19768-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2022

Abbildungen

LVI, 747 p. 269 illus., 265 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

747

Maße (L/B/H)

23,5/15,5/4,3 cm

Gewicht

1194 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-031-19768-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Computer Vision – ECCV 2022
  • Produktbild: Computer Vision – ECCV 2022
  • Learning Depth from Focus in the Wild.- Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World.- An End-to-End Transformer Model for Crowd Localization.- Few-Shot Single-View 3D Reconstruction with Memory Prior Contrastive Network.- DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection.- Adaptive Co-Teaching for Unsupervised Monocular Depth Estimation.- Fusing Local Similarities for Retrieval-Based 3D Orientation Estimation of Unseen Objects.- Lidar Point Cloud Guided Monocular 3D Object Detection.- Structural Causal 3D Reconstruction.- 3D Human Pose Estimation Using Mӧbius Graph Convolutional Networks.- Learning to Train a Point Cloud Reconstruction Network without Matching.- PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation.- Self-supervised Human Mesh Recovery with Cross-Representation Alignment.- AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction.- A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation.- PS-NeRF: Neural Inverse Rendering for Multi-View Photometric Stereo.- Share with Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency.- Towards Comprehensive Representation Enhancement in Semantics- Guided Self-Supervised Monocular Depth Estimation.- AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture.- Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers.- GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping.- Multi-modal Masked Pre-training for Monocular Panoramic Depth Completion.- GitNet: Geometric Prior-Based Transformation for Birds-Eye View Segmentation.- Learning Visibility for Robust Dense Human Body Estimation.- Towards High-Fidelity Single-View Holistic Reconstructionof Indoor Scenes.- CompNVS: Novel View Synthesis with Scene Completion.- SketchSampler: Sketch-Based 3D Reconstruction via View-Dependent Depth Sampling.- LocalBins: Improving Depth Estimation by Learning Local Distributions.- 2D GANs Meet Unsupervised Single-View 3D Reconstruction.- InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images.- Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors.- Bilateral Normal Integration.- S2Contact: Graph-Based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning.- SC-wLS: Towards Interpretable Feed-Forward Camera Re-localization.- FloatingFusion: Depth from ToF and Image-Stabilized Stereo Cameras.- DELTAR: Depth Estimation from a Light-Weight ToF Sensor and RGB Image.- 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform.- RBP-Pose: ResidualBounding Box Projection for Category-Level Pose Estimation.- Monocular 3D Object Reconstruction with GAN Inversion.- Map-Free Visual Relocalization: Metric Pose Relative to a Single Image.- Self-Distilled Feature Aggregation for Self-Supervised Monocular Depth Estimation.- Planes vs. Chairs: Category-Guided 3D Shape Learning without Any 3D Cues.