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Found 21 papers, 3 papers with code
HyperGS: Hyperspectral 3D Gaussian Splatting
We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS), based on a new latent 3D Gaussian Splatting (3DGS) technique.
PEnG: Pose-Enhanced Geo-Localisation
To solve this limitation, we propose combining cross-view geo-localisation and relative pose estimation to increase precision to a level practical for real-world application.
SpaGBOL: Spatial-Graph-Based Orientated Localisation
SpaGBOL achieves state-of-the-art accuracies on the unseen test graph - with relative Top-1 retrieval improvements on previous techniques of 11%, and 50% when filtering with Bearing Vector Matching on the SpaGBOL dataset.
Ranked #1 on
Cross-View Geo-Localisation
on VIGOR-Graph
Two Hands Are Better Than One: Resolving Hand to Hand Intersections via Occupancy Networks
This work addresses the intersection of hands by exploiting an occupancy network that represents the hand's volume as a continuous manifold.
BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints.
Ranked #1 on
Cross-View Geo-Localisation
on CVUSA 90
(R@5 metric)
Improving 3D Pose Estimation for Sign Language
Given a 2D detection of keypoints in the image, we lift the skeleton to 3D using neural networks to predict both the joint rotations and bone lengths.
Denoising Diffusion for 3D Hand Pose Estimation from Images
Hand pose estimation from a single image has many applications.
Learning Adaptive Neighborhoods for Graph Neural Networks
Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective.
AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation
To address this limitation, in this work, we propose AFT-VO, a novel transformer-based sensor fusion architecture to estimate VO from multiple sensors.
Generalizing to New Tasks via One-Shot Compositional Subgoals
In addition to improving learning efficiency for standard long-term tasks, this approach also makes it possible to perform one-shot generalization to previously unseen tasks, given only a single reference trajectory for the task in a different environment.
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning
In both cases we out-performed the SOTA by 30% in task success rate.
"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping
Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation.
Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation
To address this issue, we propose a deep sensor fusion framework which estimates vehicle motion using both pose and uncertainty estimations from multiple on-board cameras.
MDN-VO: Estimating Visual Odometry with Confidence
Visual Odometry (VO) is used in many applications including robotics and autonomous systems.
Improving Robot Localisation by Ignoring Visual Distraction
Attention is an important component of modern deep learning.
Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour
Robots need to be able to work in multiple different environments.
Markov Localisation using Heatmap Regression and Deep Convolutional Odometry
Recent advances in deep learning hardware allow large likelihood volumes to be stored directly on the GPU, along with the hardware necessary to efficiently perform GPU-bound 3D convolutions and this obviates many of the disadvantages of grid based methods.
There and Back Again: Self-supervised Multispectral Correspondence Estimation
In this work, we aim to address the dense correspondence estimation problem in a way that generalizes to more than one spectrum.
Localisation via Deep Imagination: learn the features not the map
In order to build the embedded map, we train a deep Siamese Fully Convolutional U-Net to perform dense feature extraction.
Taking the Scenic Route to 3D: Optimising Reconstruction From Moving Cameras
This approach is ill-suited for reconstruction applications, where learning about the environment is more valuable than speed of traversal.
SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?
Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world.
