Search Results for author: Maxim Maximov

Found 7 papers, 5 papers with code

Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization

1 code implementation CVPR 2021 Aysim Toker, Qunjie Zhou, Maxim Maximov, Laura Leal-Taixé

The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images.

Image Generation

4D Panoptic LiDAR Segmentation

1 code implementation CVPR 2021 Mehmet Aygün, Aljoša Ošep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixé

In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.

Multi-Object Tracking Scene Understanding +1

Focus on defocus: bridging the synthetic to real domain gap for depth estimation

1 code implementation CVPR 2020 Maxim Maximov, Kevin Galim, Laura Leal-Taixé

We are able to train our model completely on synthetic data and directly apply it to a wide range of real-world images.

 Ranked #1 on Depth Estimation on NYU-Depth V2 (RMSE metric)

Depth Estimation

CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

1 code implementation CVPR 2020 Maxim Maximov, Ismail Elezi, Laura Leal-Taixé

In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.

Action Recognition De-identification +1

Deep Appearance Maps

no code implementations ICCV 2019 Maxim Maximov, Laura Leal-Taixé, Mario Fritz, Tobias Ritschel

Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn).

LIME: Live Intrinsic Material Estimation

no code implementations CVPR 2018 Abhimitra Meka, Maxim Maximov, Michael Zollhoefer, Avishek Chatterjee, Hans-Peter Seidel, Christian Richardt, Christian Theobalt

We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input.

Frame Image-to-Image Translation +2

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