Search Results for author: Ronald Clark

Found 26 papers, 11 papers with code

TermiNeRF: Ray Termination Prediction for Efficient Neural Rendering

no code implementations5 Nov 2021 Martin Piala, Ronald Clark

Volume rendering using neural fields has shown great promise in capturing and synthesizing novel views of 3D scenes.

Neural Rendering

Waypoint Planning Networks

1 code implementation1 May 2021 Alexandru-Iosif Toma, Hussein Ali Jaafar, Hao-Ya Hsueh, Stephen James, Daniel Lenton, Ronald Clark, Sajad Saeedi

We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm.

Motion Planning

End-to-End Egospheric Spatial Memory

2 code implementations15 Feb 2021 Daniel Lenton, Stephen James, Ronald Clark, Andrew J. Davison

Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments.

General Reinforcement Learning Imitation Learning +3

Ivy: Templated Deep Learning for Inter-Framework Portability

1 code implementation4 Feb 2021 Daniel Lenton, Fabio Pardo, Fabian Falck, Stephen James, Ronald Clark

We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.

Ego-Centric Spatial Memory Networks

no code implementations ICLR 2021 Daniel James Lenton, Stephen James, Ronald Clark, Andrew Davison

With our broad demonstrations, we show that ESMN represents a useful and general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures.

Semantic Segmentation

Unsupervised Path Regression Networks

no code implementations30 Nov 2020 Michal Pándy, Daniel Lenton, Ronald Clark

We demonstrate that challenging shortest path problems can be solved via direct spline regression from a neural network, trained in an unsupervised manner (i. e. without requiring ground truth optimal paths for training).

Motion Planning

Orientation Keypoints for 6D Human Pose Estimation

no code implementations10 Sep 2020 Martin Fisch, Ronald Clark

Most realtime human pose estimation approaches are based on detecting joint positions.

14 Frame +1

LaDDer: Latent Data Distribution Modelling with a Generative Prior

1 code implementation31 Aug 2020 Shuyu Lin, Ronald Clark

In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i. e. its topology and structural properties.

Representation Learning

Scalable Uncertainty for Computer Vision with Functional Variational Inference

no code implementations CVPR 2020 Eduardo D. C. Carvalho, Ronald Clark, Andrea Nicastro, Paul H. J. Kelly

As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world.

Depth Estimation Gaussian Processes +2

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

1 code implementation5 Mar 2020 Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, Bo Yang, Andrew Markham, Niki Trigoni

In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map.

Robotics

DeepFactors: Real-Time Probabilistic Dense Monocular SLAM

1 code implementation14 Jan 2020 Jan Czarnowski, Tristan Laidlow, Ronald Clark, Andrew J. Davison

The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications.

Balancing Reconstruction Quality and Regularisation in ELBO for VAEs

no code implementations9 Sep 2019 Shuyu Lin, Stephen Roberts, Niki Trigoni, Ronald Clark

A trade-off exists between reconstruction quality and the prior regularisation in the Evidence Lower Bound (ELBO) loss that Variational Autoencoder (VAE) models use for learning.

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

1 code implementation NeurIPS 2019 Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni

The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.

Ranked #9 on 3D Instance Segmentation on S3DIS (mPrec metric)

3D Instance Segmentation Semantic Segmentation

X-Section: Cross-Section Prediction for Enhanced RGBD Fusion

no code implementations3 Mar 2019 Andrea Nicastro, Ronald Clark, Stefan Leutenegger

Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years.

3D Reconstruction

WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding

no code implementations16 Feb 2019 Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts

Variational Auto-encoders (VAEs) have been very successful as methods for forming compressed latent representations of complex, often high-dimensional, data.

LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo

no code implementations ECCV 2018 Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison

In this paper, we propose LS-Net, a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.

InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

no code implementations3 Sep 2018 Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger

Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM).

Frame Simultaneous Localization and Mapping

Learning to Solve Nonlinear Least Squares for Monocular Stereo

no code implementations ECCV 2018 Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison

In this paper, we propose a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.

Fusion++: Volumetric Object-Level SLAM

no code implementations25 Aug 2018 John McCormac, Ronald Clark, Michael Bloesch, Andrew J. Davison, Stefan Leutenegger

Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation.

Loop Closure Detection

CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM

1 code implementation CVPR 2018 Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison

Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.

CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM

2 code implementations3 Apr 2018 Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison

Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

3 code implementations25 Sep 2017 Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni

This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).

Monocular Visual Odometry Motion Estimation

3D Object Reconstruction from a Single Depth View with Adversarial Learning

2 code implementations26 Aug 2017 Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.

3D Object Reconstruction

VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization

no code implementations CVPR 2017 Ronald Clark, Sen Wang, Andrew Markham, Niki Trigoni, Hongkai Wen

Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images.

Autonomous Driving Frame +1

VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

no code implementations29 Jan 2017 Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni

In this paper we present an on-manifold sequence-to-sequence learning approach to motion estimation using visual and inertial sensors.

Motion Estimation

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