160 citations · 224 across the 11 of their papers we have counts for
11 papers
Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction
Tristan Laidlow, Jan Czarnowski, Andrea Nicastro +2
The best way to combine the results of deep learning with standard 3D reconstruction pipelines remains an open problem. While systems that pass the output of traditional multi-view…
DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions
Tristan Laidlow, Jan Czarnowski, Stefan Leutenegger
While the keypoint-based maps created by sparse monocular simultaneous localisation and mapping (SLAM) systems are useful for camera tracking, dense 3D reconstructions may be desir…
Dense RGB-D-Inertial SLAM with Map Deformations
Tristan Laidlow, Michael Bloesch, Wenbin Li +1
While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when th…
BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking
Dorian F. Henning, Tristan Laidlow, Stefan Leutenegger
Estimating human motion from video is an active research area due to its many potential applications. Most state-of-the-art methods predict human shape and posture estimates for in…
Simultaneous Localisation and Mapping with Quadric Surfaces
Tristan Laidlow, Andrew J. Davison
There are many possibilities for how to represent the map in simultaneous localisation and mapping (SLAM). While sparse, keypoint-based SLAM systems have achieved impressive levels…
ILabel: Interactive Neural Scene Labelling
Shuaifeng Zhi, Edgar Sucar, Andre Mouton +3
Joint representation of geometry, colour and semantics using a 3D neural field enables accurate dense labelling from ultra-sparse interactions as a user reconstructs a scene in rea…
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Stephen James, Kentaro Wada, Tristan Laidlow +1
We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods…
SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks
Zoe Landgraf, Raluca Scona, Tristan Laidlow +3
By estimating 3D shape and instances from a single view, we can capture information about an environment quickly, without the need for comprehensive scanning and multi-view fusion.…
In-Place Scene Labelling and Understanding with Implicit Scene Representation
Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger +1
Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes.…
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
Jan Czarnowski, Tristan Laidlow, Ronald Clark +1
The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approache…
A Primer on the Differential Calculus of 3D Orientations
Michael Bloesch, Hannes Sommer, Tristan Laidlow +8
The proper handling of 3D orientations is a central element in many optimization problems in engineering. Unfortunately many researchers and engineers struggle with the formulation…