Matthias Zwicker

Continuous Curve Textures

Repetitive patterns are ubiquitous in natural and human-made objects, an...
12/14/2020 ∙ by Peihan Tu, et al. ∙ 0 ∙ share

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Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces

Reconstructing continuous surfaces from 3D point clouds is a fundamental...
11/26/2020 ∙ by Baorui Ma, et al. ∙ 8 ∙ share

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Improved Modeling of 3D Shapes with Multi-view Depth Maps

We present a simple yet effective general-purpose framework for modeling...
09/07/2020 ∙ by Kamal Gupta, et al. ∙ 8 ∙ share

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DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

Differentiable renderers have been used successfully for unsupervised 3D...
07/12/2020 ∙ by Zhizhong Han, et al. ∙ 2 ∙ share

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Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions

Fine-grained 3D shape classification is important and research challengi...
05/26/2020 ∙ by Xinhai Liu, et al. ∙ 5 ∙ share

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LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts

Learning discriminative feature directly on point clouds is still challe...
03/18/2020 ∙ by Xinhai Liu, et al. ∙ 0 ∙ share

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LRC-Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts

Learning discriminative feature directly on point clouds is still challe...
03/18/2020 ∙ by Xinhai Liu, et al. ∙ 0 ∙ share

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SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates

Structure learning for 3D shapes is vital for 3D computer vision. State-...
03/12/2020 ∙ by Zhizhong Han, et al. ∙ 21 ∙ share

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ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints

OR-constrained (ORC) graphical user interface layouts unify conventional...
02/23/2020 ∙ by Yue Jiang, et al. ∙ 0 ∙ share

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Learning Generative Models using Denoising Density Estimators

Learning generative probabilistic models that can estimate the continuou...
01/08/2020 ∙ by Siavash A. Bigdeli, et al. ∙ 27 ∙ share

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Learning to Generate Dense Point Clouds with Textures on Multiple Categories

3D reconstruction from images is a core problem in computer vision. With...
12/22/2019 ∙ by Tao Hu, et al. ∙ 21 ∙ share

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SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

We propose SDFDiff, a novel approach for image-based shape optimization ...
12/15/2019 ∙ by Yue Jiang, et al. ∙ 53 ∙ share

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3D Shape Completion with Multi-view Consistent Inference

3D shape completion is important to enable machines to perceive the comp...
11/28/2019 ∙ by Tao Hu, et al. ∙ 19 ∙ share

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L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

Auto-encoder is an important architecture to understand point clouds in ...
08/02/2019 ∙ by Xinhai Liu, et al. ∙ 2 ∙ share

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ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences

3D shape captioning is a challenging application in 3D shape understandi...
07/31/2019 ∙ by Zhizhong Han, et al. ∙ 1 ∙ share

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Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction

Unsupervised feature learning for point clouds has been vital for large-...
07/30/2019 ∙ by Zhizhong Han, et al. ∙ 0 ∙ share

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Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

Deep learning has achieved remarkable results in 3D shape analysis by le...
05/18/2019 ∙ by Zhizhong Han, et al. ∙ 2 ∙ share

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3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

Learning global features by aggregating information over multiple views ...
05/17/2019 ∙ by Zhizhong Han, et al. ∙ 0 ∙ share

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Render4Completion: Synthesizing Multi-view Depth Maps for 3D Shape Completion

We propose a novel approach for 3D shape completion by synthesizing mult...
04/17/2019 ∙ by Tao Hu, et al. ∙ 0 ∙ share

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Smart, Deep Copy-Paste

In this work, we propose a novel system for smart copy-paste, enabling t...
03/15/2019 ∙ by Tiziano Portenier, et al. ∙ 79 ∙ share

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Understanding the (un)interpretability of natural image distributions using generative models

Probability density estimation is a classical and well studied problem, ...
01/06/2019 ∙ by Ryen Krusinga, et al. ∙ 20 ∙ share

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Video Synthesis from a Single Image and Motion Stroke

In this paper, we propose a new method to automatically generate a video...
12/05/2018 ∙ by Qiyang Hu, et al. ∙ 10 ∙ share

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Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

A recent method employs 3D voxels to represent 3D shapes, but this limit...
11/07/2018 ∙ by Zhizhong Han, et al. ∙ 8 ∙ share

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View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions

In this paper we present a novel unsupervised representation learning ap...
11/07/2018 ∙ by Zhizhong Han, et al. ∙ 10 ∙ share

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Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Exploring contextual information in the local region is important for sh...
11/06/2018 ∙ by Xinhai Liu, et al. ∙ 2 ∙ share

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Learning to Importance Sample in Primary Sample Space

Importance sampling is one of the most widely used variance reduction st...
08/23/2018 ∙ by Quan Zheng, et al. ∙ 2 ∙ share

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Specular-to-Diffuse Translation for Multi-View Reconstruction

Most multi-view 3D reconstruction algorithms, especially when shape-from...
07/14/2018 ∙ by Shihao Wu, et al. ∙ 2 ∙ share

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FaceShop: Deep Sketch-based Face Image Editing

We present a novel system for sketch-based face image editing, enabling ...
04/24/2018 ∙ by Tiziano Portenier, et al. ∙ 0 ∙ share

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Disentangling Factors of Variation by Mixing Them

We propose an unsupervised approach to learn image representations that ...
11/20/2017 ∙ by Qiyang Hu, et al. ∙ 0 ∙ share

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Challenges in Disentangling Independent Factors of Variation

We study the problem of building models that disentangle independent fac...
11/07/2017 ∙ by Attila Szabó, et al. ∙ 0 ∙ share

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Deep Mean-Shift Priors for Image Restoration

In this paper we introduce a natural image prior that directly represent...
09/12/2017 ∙ by Siavash Arjomand Bigdeli, et al. ∙ 0 ∙ share

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Image Restoration using Autoencoding Priors

We propose to leverage denoising autoencoder networks as priors to addre...
03/29/2017 ∙ by Siavash Arjomand Bigdeli, et al. ∙ 0 ∙ share

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Temporally Consistent Motion Segmentation from RGB-D Video

We present a method for temporally consistent motion segmentation from R...
08/16/2016 ∙ by Peter Bertholet, et al. ∙ 0 ∙ share

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Matthias Zwicker

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