Search Results for author: Joni-Kristian Kämäräinen

Found 21 papers, 7 papers with code

PlaceNav: Topological Navigation through Place Recognition

no code implementations29 Sep 2023 Lauri Suomela, Jussi Kalliola, Harry Edelman, Joni-Kristian Kämäräinen

Recent results suggest that splitting topological navigation into robot-independent and robot-specific components improves navigation performance by enabling the robot-independent part to be trained with data collected by robots of different types.

Computational Efficiency Visual Place Recognition

Depth-Aware Image Compositing Model for Parallax Camera Motion Blur

1 code implementation16 Mar 2023 German F. Torres, Joni-Kristian Kämäräinen

The (forward) model produces realistic motion blur from a single image, depth map, and camera trajectory.


RGBD Object Tracking: An In-depth Review

1 code implementation26 Mar 2022 Jinyu Yang, Zhe Li, Song Yan, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen, Ling Shao

Particularly, we are the first to provide depth quality evaluation and analysis of tracking results in depth-friendly scenarios in RGBD tracking.

Object Object Tracking

Benchmarking Visual Localization for Autonomous Navigation

1 code implementation24 Mar 2022 Lauri Suomela, Jussi Kalliola, Atakan Dag, Harry Edelman, Joni-Kristian Kämäräinen

The experimental part of the paper studies the effects of four such variables by evaluating state-of-the-art visual localization methods as part of the motion planning module of an autonomous navigation stack.

Autonomous Navigation Benchmarking +3

State-Conditioned Adversarial Subgoal Generation

no code implementations24 Jan 2022 Vivienne Huiling Wang, Joni Pajarinen, Tinghuai Wang, Joni-Kristian Kämäräinen

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction.

Continuous Control Decision Making +3

DepthTrack : Unveiling the Power of RGBD Tracking

1 code implementation31 Aug 2021 Song Yan, Jinyu Yang, Jani Käpylä, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen

RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors have become popular in many application fields such as robotics. However, the best RGBD trackers are extensions of the state-of-the-art deep RGB trackers.

Object Tracking

Neural Network Controller for Autonomous Pile Loading Revised

no code implementations23 Mar 2021 Wenyan Yang, Nataliya Strokina, Nikolay Serbenyuk, Joni Pajarinen, Reza Ghabcheloo, Juho Vihonen, Mohammad M. Aref, Joni-Kristian Kämäräinen

We have recently proposed two pile loading controllers that learn from human demonstrations: a neural network (NNet) [1] and a random forest (RF) controller [2].

Learning Anthropometry from Rendered Humans

no code implementations7 Jan 2021 Song Yan, Joni-Kristian Kämäräinen

To circumvent the data bottleneck, we introduce a new 3D scan dataset of 2, 675 female and 1, 474 male scans.

Medical Diagnosis

Fast Fourier Intrinsic Network

no code implementations9 Nov 2020 Yanlin Qian, Miaojing Shi, Joni-Kristian Kämäräinen, Jiri Matas

We address the problem of decomposing an image into albedo and shading.

A Benchmark for Temporal Color Constancy

3 code implementations8 Mar 2020 Yanlin Qian, Jani Käpylä, Joni-Kristian Kämäräinen, Samu Koskinen, Jiri Matas

The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color.

Color Constancy

DAL -- A Deep Depth-aware Long-term Tracker

no code implementations2 Dec 2019 Yanlin Qian, Alan Lukežič, Matej Kristan, Joni-Kristian Kämäräinen, Jiri Matas

In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run.

Anthropometric clothing measurements from 3D body scans

no code implementations2 Nov 2019 Song Yan, Johan Wirta, Joni-Kristian Kämäräinen

In the second stage, a pre-defined body model is fitted to the captured point cloud.

Object Pose Estimation in Robotics Revisited

no code implementations6 Jun 2019 Antti Hietanen, Jyrki Latokartano, Alessandro Foi, Roel Pieters, Ville Kyrki, Minna Lanz, Joni-Kristian Kämäräinen

The evaluation metric is based on non-parametric probability density that is estimated from samples of a real physical setup.

3D Pose Estimation 6D Pose Estimation +3

Flash Lightens Gray Pixels

no code implementations27 Feb 2019 Yanlin Qian, Song Yan, Joni-Kristian Kämäräinen, Jiri Matas

In the real world, a scene is usually cast by multiple illuminants and herein we address the problem of spatial illumination estimation.

On Finding Gray Pixels

2 code implementations CVPR 2019 Yanlin Qian, Joni-Kristian Kämäräinen, Jarno Nikkanen, Jiri Matas

We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation.

Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation

no code implementations17 Aug 2018 Junsheng Fu, Said Pertuz, Jiri Matas, Joni-Kristian Kämäräinen

We consider a single-query 6-DoF camera pose estimation with reference images and a point cloud, i. e. the problem of estimating the position and orientation of a camera by using reference images and a point cloud.

Camera Pose Estimation Pose Estimation

Revisiting Gray Pixel for Statistical Illumination Estimation

1 code implementation22 Mar 2018 Yanlin Qian, Said Pertuz, Jarno Nikkanen, Joni-Kristian Kämäräinen, Jiri Matas

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering.

Clustering Color Constancy

Depth Masked Discriminative Correlation Filter

no code implementations26 Feb 2018 Uğur Kart, Joni-Kristian Kämäräinen, Jiří Matas, Lixin Fan, Francesco Cricri

Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications.

Object Tracking

Convolutional Low-Resolution Fine-Grained Classification

no code implementations15 Mar 2017 Dingding Cai, Ke Chen, Yanlin Qian, Joni-Kristian Kämäräinen

Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution.

Classification Fine-Grained Image Classification +2

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