Search Results for author: Muhamad Risqi U. Saputra

Found 10 papers, 2 papers with code

VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization

1 code implementation12 Mar 2020 Kaichen Zhou, Changhao Chen, Bing Wang, Muhamad Risqi U. Saputra, Niki Trigoni, Andrew Markham

We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.

Camera Relocalization Visual Localization

SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation

no code implementations22 Nov 2019 Yasin Almalioglu, Mehmet Turan, Alp Eren Sari, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmão, Andrew Markham, Niki Trigoni

In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation.

Depth Estimation Pose Estimation +3

DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

no code implementations13 Oct 2019 Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao, Pedro Gusmao, Bo Yang, Changhao Chen, Andrew Markham, Niki Trigoni

There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images.

Translation Visual Odometry

Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning

no code implementations25 Mar 2019 Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Sen Wang, Andrew Markham, Niki Trigoni

Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data.

Monocular Visual Odometry Optical Flow Estimation

GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

no code implementations16 Sep 2018 Yasin Almalioglu, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Andrew Markham, Niki Trigoni

In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant.

Depth Estimation Monocular Visual Odometry +1

Cannot find the paper you are looking for? You can Submit a new open access paper.