Andreas Geiger

Attacking Optical Flow

Deep neural nets achieve state-of-the-art performance on the problem of ...
10/22/2019 ∙ by Anurag Ranjan, et al. ∙ 33 ∙ share

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RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials

In this paper, we consider the problem of reconstructing a dense 3D mode...
01/06/2019 ∙ by Despoina Paschalidou, et al. ∙ 28 ∙ share

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Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

In recent years, Generative Adversarial Networks have achieved impressiv...
12/11/2019 ∙ by Yiyi Liao, et al. ∙ 24 ∙ share

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Robust Dense Mapping for Large-Scale Dynamic Environments

We present a stereo-based dense mapping algorithm for large-scale dynami...
05/07/2019 ∙ by Ioan Andrei Bârsan, et al. ∙ 22 ∙ share

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Convolutional Occupancy Networks

Recently, implicit neural representations have gained popularity for lea...
03/10/2020 ∙ by Songyou Peng, et al. ∙ 12 ∙ share

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Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image

Humans perceive the 3D world as a set of distinct objects that are chara...
04/02/2020 ∙ by Despoina Paschalidou, et al. ∙ 9 ∙ share

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Taking a Deeper Look at the Inverse Compositional Algorithm

In this paper, we provide a modern synthesis of the classic inverse comp...
12/17/2018 ∙ by Zhaoyang Lv, et al. ∙ 8 ∙ share

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Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids

Abstracting complex 3D shapes with parsimonious part-based representatio...
04/22/2019 ∙ by Despoina Paschalidou, et al. ∙ 8 ∙ share

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Self-Supervised Linear Motion Deblurring

Motion blurry images challenge many computer vision algorithms, e.g, fea...
02/10/2020 ∙ by Peidong Liu, et al. ∙ 8 ∙ share

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Texture Fields: Learning Texture Representations in Function Space

In recent years, substantial progress has been achieved in learning-base...
05/17/2019 ∙ by Michael Oechsle, et al. ∙ 6 ∙ share

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Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3...
12/10/2018 ∙ by Lars Mescheder, et al. ∙ 2 ∙ share

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Learning Implicit Surface Light Fields

Implicit representations of 3D objects have recently achieved impressive...
03/27/2020 ∙ by Michael Oechsle, et al. ∙ 1 ∙ share

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Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes

The success of deep learning in computer vision is based on availability...
08/04/2017 ∙ by Hassan Abu Alhaija, et al. ∙ 0 ∙ share

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Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

We present a new notion of probabilistic duality for random variables in...
11/21/2016 ∙ by Lars Mescheder, et al. ∙ 0 ∙ share

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OctNetFusion: Learning Depth Fusion from Data

In this paper, we present a learning based approach to depth fusion, i.e...
04/04/2017 ∙ by Gernot Riegler, et al. ∙ 0 ∙ share

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OctNet: Learning Deep 3D Representations at High Resolutions

We present OctNet, a representation for deep learning with sparse 3D dat...
11/15/2016 ∙ by Gernot Riegler, et al. ∙ 0 ∙ share

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Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Semantic annotations are vital for training models for object recognitio...
11/10/2015 ∙ by Jun Xie, et al. ∙ 0 ∙ share

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FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

One of the most popular approaches to multi-target tracking is tracking-...
07/23/2014 ∙ by Philip Lenz, et al. ∙ 0 ∙ share

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Semantic Visual Localization

Robust visual localization under a wide range of viewing conditions is a...
12/15/2017 ∙ by Johannes L. Schönberger, et al. ∙ 0 ∙ share

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On the Integration of Optical Flow and Action Recognition

Most of the top performing action recognition methods use optical flow a...
12/22/2017 ∙ by Laura Sevilla-Lara, et al. ∙ 0 ∙ share

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Learning 3D Shape Completion under Weak Supervision

We address the problem of 3D shape completion from sparse and noisy poin...
05/18/2018 ∙ by David Stutz, et al. ∙ 0 ∙ share

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Which Training Methods for GANs do actually Converge?

Recent work has shown local convergence of GAN training for absolutely c...
01/13/2018 ∙ by Lars Mescheder, et al. ∙ 0 ∙ share

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PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds

Despite significant progress in image-based 3D scene flow estimation, th...
06/06/2018 ∙ by Aseem Behl, et al. ∙ 0 ∙ share

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Conditional Affordance Learning for Driving in Urban Environments

Most existing approaches to autonomous driving fall into one of two cate...
06/18/2018 ∙ by Axel Sauer, et al. ∙ 0 ∙ share

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Geometric Image Synthesis

The task of generating natural images from 3D scenes has been a long sta...
09/12/2018 ∙ by Hassan Abu Alhaija, et al. ∙ 0 ∙ share

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Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System

Project AutoVision aims to develop localization and 3D scene perception ...
09/14/2018 ∙ by Lionel Heng, et al. ∙ 0 ∙ share

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Real-Time Dense Mapping for Self-driving Vehicles using Fisheye Cameras

We present a real-time dense geometric mapping algorithm for large-scale...
09/17/2018 ∙ by Zhaopeng Cui, et al. ∙ 0 ∙ share

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MOTS: Multi-Object Tracking and Segmentation

This paper extends the popular task of multi-object tracking to multi-ob...
02/10/2019 ∙ by Paul Voigtlaender, et al. ∙ 0 ∙ share

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Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

Learning-based 3D reconstruction methods have shown impressive results. ...
12/16/2019 ∙ by Michael Niemeyer, et al. ∙ 0 ∙ share

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Label Efficient Visual Abstractions for Autonomous Driving

It is well known that semantic segmentation can be used as an effective ...
05/20/2020 ∙ by Aseem Behl, et al. ∙ 0 ∙ share

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Andreas Geiger

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