Search Results for author: René Schuster

Found 24 papers, 3 papers with code

JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation

no code implementations30 Nov 2023 Shishir Muralidhara, Sravan Kumar Jagadeesh, René Schuster, Didier Stricker

Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity.

Panoptic Segmentation Part-aware Panoptic Segmentation +1

Multi-task Fusion for Efficient Panoptic-Part Segmentation

no code implementations15 Dec 2022 Sravan Kumar Jagadeesh, René Schuster, Didier Stricker

For CPP, the PartPQ of our proposed model with joint fusion surpasses the previous state-of-the-art by 1. 6 and 4. 7 percentage points for all areas and segments with parts, respectively.

Part-aware Panoptic Segmentation Representation Learning +1

Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time

no code implementations28 Nov 2022 Michael Fürst, Priyash Bhugra, René Schuster, Didier Stricker

Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded.

object-detection Object Detection

Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation

1 code implementation13 Oct 2022 Dipam Goswami, René Schuster, Joost Van de Weijer, Didier Stricker

In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.

Overlapped 100-10 Overlapped 100-5 +7

Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences

no code implementations30 Jun 2022 Katharina Bendig, René Schuster, Didier Stricker

In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction.

RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds

no code implementations1 Apr 2022 Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani, Didier Stricker

The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density.

Scene Flow Estimation

Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

no code implementations25 Oct 2021 Kumail Raza, René Schuster, Didier Stricker

This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap by allowing it to adopt any stereo matching network to make it fast, more efficient and scalable while keeping comparable accuracy.

Stereo Matching

MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow

no code implementations21 Oct 2020 René Schuster, Christian Unger, Didier Stricker

Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i. e. using a single sensor.

Depth Estimation Optical Flow Estimation

HPERL: 3D Human Pose Estimation from RGB and LiDAR

1 code implementation16 Oct 2020 Michael Fürst, Shriya T. P. Gupta, René Schuster, Oliver Wasenmüller, Didier Stricker

In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving.

3D Human Pose Estimation Action Recognition +2

SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation

no code implementations21 Aug 2020 René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker

Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision.

Depth Completion Optical Flow Estimation

DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

no code implementations18 Aug 2020 Rishav, Ramy Battrawy, René Schuster, Oliver Wasenmüller, Didier Stricker

In this paper, we present DeepLiDARFlow, a novel deep learning architecture which fuses high level RGB and LiDAR features at multiple scales in a monocular setup to predict dense scene flow.

3D Reconstruction Scene Flow Estimation

ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching

no code implementations22 Jun 2020 Rishav, René Schuster, Ramy Battrawy, Oliver Wasenmüller, Didier Stricker

Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features.

Optical Flow Estimation Scene Flow Estimation

LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images

no code implementations31 Oct 2019 Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, Didier Stricker

We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images.

Scene Flow Estimation

PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation

1 code implementation12 Apr 2019 Rohan Saxena, René Schuster, Oliver Wasenmüller, Didier Stricker

In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching.

Optical Flow Estimation Scene Flow Estimation +2

An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching

no code implementations12 Apr 2019 René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker

Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function, and the construction of training schedules is important to get the most out of a model.

FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation

no code implementations9 May 2018 René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker

Thus, we propose in this paper FlowFields++, where we combine the accurate matches of Flow Fields with a robust interpolation.

Optical Flow Estimation

SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences

no code implementations27 Oct 2017 René Schuster, Oliver Wasenmüller, Georg Kuschk, Christian Bailer, Didier Stricker

While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches.

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