11 papers
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…
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…
Non-Invasive 3D Wound Measurement with RGB-D Imaging
Lena Harkämper, Leo Lebrat, David Ahmedt-Aristizabal +3
Chronic wound monitoring and management require accurate and efficient wound measurement methods. This paper presents a fast, non-invasive 3D wound measurement algorithm based on R…
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…
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…
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…