Search Results for author: Stuart Golodetz

Found 16 papers, 7 papers with code

Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes

1 code implementation ECCV 2020 Johanna Wald, Torsten Sattler, Stuart Golodetz, Tommaso Cavallari, Federico Tombari

In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes.

Camera Relocalization

Calibrating Deep Neural Networks using Focal Loss

2 code implementations NeurIPS 2020 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania

To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function.

The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks

no code implementations25 Sep 2019 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.

ShardNet: One Filter Set to Rule Them All

no code implementations25 Sep 2019 Saumya Jetley, Tommaso Cavallari, Philip Torr, Stuart Golodetz

Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.

Learning Theory

Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC

no code implementations17 Jul 2019 Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi Di Stefano, Simon Walker, Philip H. S. Torr

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs.

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

1 code implementation27 May 2019 Laurynas Miksys, Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

The STS model can run at 35 FPS on a high-end desktop, but its accuracy is significantly worse than that of offline state-of-the-art methods.

Autonomous Driving Data Augmentation +3

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

1 code implementation29 Oct 2018 Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr

The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.

Pose Estimation

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

no code implementations25 Jan 2018 Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.

3D Reconstruction

InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure

1 code implementation2 Aug 2017 Victor Adrian Prisacariu, Olaf Kähler, Stuart Golodetz, Michael Sapienza, Tommaso Cavallari, Philip H. S. Torr, David W. Murray

Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems.

3D Reconstruction Simultaneous Localization and Mapping

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

no code implementations CVPR 2017 Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr

Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.

Camera Relocalization

Straight to Shapes: Real-time Detection of Encoded Shapes

1 code implementation CVPR 2017 Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors.

Denoising Object Detection

Joint Object-Material Category Segmentation from Audio-Visual Cues

no code implementations10 Jan 2016 Anurag Arnab, Michael Sapienza, Stuart Golodetz, Julien Valentin, Ondrej Miksik, Shahram Izadi, Philip Torr

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.

Staple: Complementary Learners for Real-Time Tracking

2 code implementations CVPR 2016 Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.

Visual Object Tracking

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