Search Results for author: René Ranftl

Found 16 papers, 7 papers with code

Monocular Visual-Inertial Depth Estimation

1 code implementation21 Mar 2023 Diana Wofk, René Ranftl, Matthias Müller, Vladlen Koltun

We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone.

Depth Completion Monocular Depth Estimation

Learning High-Speed Flight in the Wild

1 code implementation11 Oct 2021 Antonio Loquercio, Elia Kaufmann, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza

Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.

Vocal Bursts Intensity Prediction

An Analysis of Super-Net Heuristics in Weight-Sharing NAS

no code implementations4 Oct 2021 Kaicheng Yu, René Ranftl, Mathieu Salzmann

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware.

Neural Architecture Search

Vision Transformers for Dense Prediction

15 code implementations ICCV 2021 René Ranftl, Alexey Bochkovskiy, Vladlen Koltun

We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks.

Monocular Depth Estimation Semantic Segmentation

Deep Drone Acrobatics

1 code implementation10 Jun 2020 Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza

In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.


Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

14 code implementations2 Jul 2019 René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun

In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks.

Monocular Depth Estimation

High Speed and High Dynamic Range Video with an Event Camera

1 code implementation15 Jun 2019 Henri Rebecq, René Ranftl, Vladlen Koltun, Davide Scaramuzza

In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors.

Vocal Bursts Intensity Prediction

Events-to-Video: Bringing Modern Computer Vision to Event Cameras

no code implementations CVPR 2019 Henri Rebecq, René Ranftl, Vladlen Koltun, Davide Scaramuzza

Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events.

A higher-order MRF based variational model for multiplicative noise reduction

no code implementations21 Apr 2014 Yunjin Chen, Wensen Feng, René Ranftl, Hong Qiao, Thomas Pock

The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.

Image Restoration

A bi-level view of inpainting - based image compression

no code implementations16 Jan 2014 Yunjin Chen, René Ranftl, Thomas Pock

Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression.

Descriptive Image Compression

Revisiting loss-specific training of filter-based MRFs for image restoration

no code implementations16 Jan 2014 Yunjin Chen, Thomas Pock, René Ranftl, Horst Bischof

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision.

Image Denoising Image Restoration

Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs

no code implementations13 Jan 2014 Yunjin Chen, René Ranftl, Thomas Pock

Numerical experiments show that our trained models clearly outperform existing analysis operator learning approaches and are on par with state-of-the-art image denoising algorithms.

Image Denoising Image Restoration +1

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