2 citations · 3 across the 16 of their papers we have counts for
10 papers · 1 filter
Artemis: Geometry-Grounded Multi-Agent Driving World Models with Shared 3D State and Progressive Memory Update
Sitian Shen, Jiuming Liu, Mengmeng Liu +7
Recent video world models have witnessed the paradigm shift from single-agent to multi-agent involvements, which can reveal more complicated dynamics and cross-agent interaction in…
Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys
Halil Sina Kelebek, Julia Hindel, Kobus Hoffman +9
Animal re-identification (ReID) in camera-trap surveys remains challenging due to low image quality, strong variation in illumination and viewpoint, and highly imbalanced numbers o…
PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving
Kyuhwan Yeon, Benjamin Ramtoula, Daniele De Martini
Most end-to-end autonomous driving methods rely solely on instantaneous sensor observations, limiting them to reactive behavior without the anticipatory foresight human drivers emp…
Neural-MMGS: Multi-modal Neural Gaussian Splats for Large-Scale Scene Reconstruction
Sitian Shen, Georgi Pramatarov, Yifu Tao +1
This paper proposes Neural-MMGS, a novel neural 3DGS framework for multimodal large-scale scene reconstruction that fuses multiple sensing modalities in a per-gaussian compact, lea…
MinkOcc: Towards real-time label-efficient semantic occupancy prediction
Samuel Sze, Daniele De Martini, Lars Kunze
Developing 3D semantic occupancy prediction models often relies on dense 3D annotations for supervised learning, a process that is both labor and resource-intensive, underscoring t…
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition
Benjamin Ramtoula, Daniele De Martini, Matthew Gadd +1
This paper adapts a general dataset representation technique to produce robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world mobile robot localisation. T…
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation
Georgi Pramatarov, Matthew Gadd, Paul Newman +1
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all poi…
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
David S. W. Williams, Daniele De Martini, Matthew Gadd +1
Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an importa…
Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
David S. W. Williams, Matthew Gadd, Paul Newman +1
This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation mod…
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation
Matthew Gadd, Benjamin Ramtoula, Daniele De Martini +1
Recalling the most relevant visual memories for localisation or understanding a priori the likely outcome of localisation effort against a particular visual memory is useful for ef…