Search Results for author: Tommaso Cavallari

Found 14 papers, 4 papers with code

Map-Relative Pose Regression for Visual Re-Localization

1 code implementation15 Apr 2024 Shuai Chen, Tommaso Cavallari, Victor Adrian Prisacariu, Eric Brachmann

We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion.

Novel View Synthesis regression

Accelerated Coordinate Encoding: Learning to Relocalize in Minutes using RGB and Poses

no code implementations CVPR 2023 Eric Brachmann, Tommaso Cavallari, Victor Adrian Prisacariu

We start from the obvious: a relocalization network can be split in a scene-agnostic feature backbone, and a scene-specific prediction head.

Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds

no code implementations30 Jun 2021 Mihaela Cătălina Stoian, Tommaso Cavallari

Additionally, we show that it can be deployed on partial scans of objects in a real-world pipeline to improve the outputs of a 3D object detector.


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

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.

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 regression

Volume-based Semantic Labeling with Signed Distance Functions

no code implementations13 Nov 2015 Tommaso Cavallari, Luigi Di Stefano

Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks.

Segmentation Semantic Segmentation +1

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