20 citations · 50 across the 27 of their papers we have counts for
29 papers
REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning
Akila Ayanthi, Darshana Priyasad, Tharindu Fernando +3
Regularisation-based class-incremental learning (CIL) methods control the stability-plasticity trade-off through fixed coefficients tuned offline, independently of the continual le…
OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception
Binh Long Nguyen, Kien Nguyen, Clinton Fookes +1
Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping approaches enable high-fidelity…
Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation
Saimunur Rahman, Sagun Singh Shrestha, Abdelwahed Khamis +1
Automotive spinning FMCW radar provides dense, sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous navigation.…
Geometry-Conditioned Visual Place Recognition in Natural Environments
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya +3
Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial appearance and viewpoint vari…
NEO: NeRF It Once, Edit It Many Times for Continuous Object Manipulation
Mikołaj Zieliński, David Hall, Dominik Belter +1
In this paper, we present NEO, a unified framework providing language-guided NeRF editing for robotic manipulation. Our paper introduces (i) a language-guided object removal that c…
GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
Peter Bohm, Saimunur Rahman, Abdelwahed Khamis +3
Visual navigation policies built on large pretrained models increasingly rely on large cross-embodiment datasets and expressive action-generation mechanisms. In this paper, we ask…
TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation
Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy +4
Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point…
Spatially Stratified Distillation for Heterogeneous Radar Place Recognition
Sagun Singh Shrestha, Samuel Harding, Abdelwahed Khamis +2
Scalable, all-weather place recognition increasingly relies on heterogeneous radar place recognition to bridge diverse hardware platforms. A notable application is matching queries…
Visual Place Recognition in Forests with Depth-Aware Distillation
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya +3
Visual place recognition in natural forest environments remains challenging due to repetitive vegetation, weak structural cues, and significant appearance variation across traversa…
Cross-Modal Benchmarking for Robotic Perception in Natural Environments
David Hall, Joshua Knights, Mark Cox +1
Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban env…
Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting
Binh Long Nguyen, Kien Nguyen, Sridha Sridharan +2
We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior work depends on 2D rendering…
WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments
Joshua Knights, Joseph Reid, Kaushik Roy +3
Recent years have seen a significant increase in demand for robotic solutions in unstructured natural environments, alongside growing interest in bridging 2D and 3D scene understan…
Dual-Domain Masked Image Modeling: A Self-Supervised Pretraining Strategy Using Spatial and Frequency Domain Masking for Hyperspectral Data
Shaheer Mohamed, Tharindu Fernando, Sridha Sridharan +2
Hyperspectral images (HSIs) capture rich spectral signatures that reveal vital material properties, offering broad applicability across various domains. However, the scarcity of la…
Spectral-Enhanced Transformers: Leveraging Large-Scale Pretrained Models for Hyperspectral Object Tracking
Shaheer Mohamed, Tharindu Fernando, Sridha Sridharan +2
Hyperspectral object tracking using snapshot mosaic cameras is emerging as it provides enhanced spectral information alongside spatial data, contributing to a more comprehensive un…
Inductive Graph Few-shot Class Incremental Learning
Yayong Li, Peyman Moghadam, Can Peng +2
Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves lea…
Pair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers
Stephen Hausler, Peyman Moghadam
In this work we propose a novel joint training method for Visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The…
A Deeper Look into Second-Order Feature Aggregation for LiDAR Place Recognition
Saimunur Rahman, Peyman Moghadam
Efficient LiDAR Place Recognition (LPR) compresses dense pointwise features into compact global descriptors. While first-order aggregators such as GeM and NetVLAD are widely used,…
Point-PNG: Conditional Pseudo-Negatives Generation for Point Cloud Pre-Training
Sutharsan Mahendren, Saimunur Rahman, Piotr Koniusz +4
We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both…
SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
Joshua Knights, Sebastián Barbas Laina, Peyman Moghadam +1
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We p…
Object Registration in Neural Fields
David Hall, Stephen Hausler, Sutharsan Mahendren +1
Neural fields provide a continuous scene representation of 3D geometry and appearance in a way which has great promise for robotics applications. One functionality that unlocks uni…
Towards Long-term Robotics in the Wild
Stephen Hausler, Ethan Griffiths, Milad Ramezani +1
In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets…
Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields
Stephen Hausler, David Hall, Sutharsan Mahendren +1
Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit re…
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Kavisha Vidanapathirana, Joshua Knights, Stephen Hausler +8
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.…
TULIP: Transformer for Upsampling of LiDAR Point Clouds
Bin Yang, Patrick Pfreundschuh, Roland Siegwart +3
LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles, due to the sparse and irregular structure of large-scale scene contexts. Recent…