3 citations · 6 across the 16 of their papers we have counts for
18 papers
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…
Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection. We show that this protocol can become ill-posed when…
High-Dimensional Latents Should Be Diagnosed Through Phase Structure
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory. The paper has two components. First, we formalize a latent-space spin-glass di…
First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction
Remi Chierchia, Léo Lebrat, David Ahmedt-Aristizabal +3
Neural Surface Reconstruction has become a standard methodology for indoor 3D reconstruction, with Signed Distance Functions (SDFs) proving particularly effective for representing…
When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply preconditions, and events satisfy te…
In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
Wenhui Xiao, Ethan Goan, Rodrigo Santa Cruz +4
Using accurate depth priors in 3D Gaussian Splatting helps mitigate artifacts caused by sparse training data and textureless surfaces. However, acquiring accurate depth maps requir…
Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
Akila Pemasiri, Ethan Goan, Glen Lichtwark +3
This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the pr…
NC-Reg : Neural Cortical Maps for Rigid Registration
Ines Vati, Pierrick Bourgeat, Rodrigo Santa Cruz +4
We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and…
Multi-View Consistent Wound Segmentation With Neural Fields
Remi Chierchia, Léo Lebrat, David Ahmedt-Aristizabal +4
Wound care is often challenged by the economic and logistical burdens that consistently afflict patients and hospitals worldwide. In recent decades, healthcare professionals have s…
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can hope to detect out-of-distributio…
Physics-Informed Operator Learning for Hemodynamic Modeling
Ryan Chappell, Chayan Banerjee, Kien Nguyen +1
Accurate modeling of personalized cardiovascular dynamics is crucial for non-invasive monitoring and therapy planning. State-of-the-art physics-informed neural network (PINN) appro…
Wound3DAssist: A Practical Framework for 3D Wound Assessment
Remi Chierchia, Rodrigo Santa Cruz, Léo Lebrat +7
Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digi…
Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
Chayan Banerjee, Kien Nguyen, Clinton Fookes
Optimizing the mining process -- particularly truck dispatch scheduling -- is a key driver of efficiency in open-pit operations. However, the dynamic and stochastic nature of these…
Neural Radiance Fields for the Real World: A Survey
Wenhui Xiao, Remi Chierchia, Rodrigo Santa Cruz +5
Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields…
Efficient Localization of Directional Emitters via Joint Beampattern Estimation
Fraser Williams, Akila Pemasiri, Dhammika Jayalath +2
Localization methods designed for omnidirectional emitters degrade on directional sources, where directive beampatterns induce pronounced received signal strength (RSS) modulations…
PINNs for Medical Image Analysis: A Survey
Chayan Banerjee, Kien Nguyen, Olivier Salvado +2
The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). By integrating fundamental knowledge and governing physical l…
Size and Smoothness Aware Adaptive Focal Loss for Small Tumor Segmentation
Md Rakibul Islam, Riad Hassan, Abdullah Nazib +3
Deep learning has achieved remarkable accuracy in medical image segmentation, particularly for larger structures with well-defined boundaries. However, its effectiveness can be cha…
CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
Léo Lebrat, Rodrigo Santa Cruz, Frédéric de Gournay +5
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object. To…