32 citations · 39 across the 11 of their papers we have counts for
13 papers
Remove360: Benchmarking Residuals After Object Removal in 3D Gaussian Splatting
Simona Kocour, Assia Benbihi, Torsten Sattler
An object can disappear from a 3D scene, yet still be detectable. Even after visual removal, modern vision models may infer what was originally present. In this work, we introduce…
Is there anything left? Measuring semantic residuals of objects removed from 3D Gaussian Splatting
Simona Kocour, Assia Benbihi, Aikaterini Adam +1
Searching in and editing 3D scenes has become extremely intuitive with trainable scene representations that allow linking human concepts to elements in the scene. These operations…
EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting
Kunal Chelani, Assia Benbihi, Torsten Sattler +1
With their meaningful geometry and their omnipresence in the 3D world, edges are extremely useful primitives in computer vision. 3D edges comprise of lines and curves, and methods…
Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information
Kunal Chelani, Assia Benbihi, Fredrik Kahl +2
Rapid growth in the popularity of AR/VR/MR applications and cloud-based visual localization systems has given rise to an increased focus on the privacy of user content in the local…
Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation
Varun Burde, Assia Benbihi, Pavel Burget +1
Object pose estimation is essential to many industrial applications involving robotic manipulation, navigation, and augmented reality. Current generalizable object pose estimators,…
Differentiable Product Quantization for Memory Efficient Camera Relocalization
Zakaria Laskar, Iaroslav Melekhov, Assia Benbihi +2
Camera relocalization relies on 3D models of the scene with a large memory footprint that is incompatible with the memory budget of several applications. One solution to reduce the…
Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers
Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale +1
Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obt…
Object-Guided Day-Night Visual Localization in Urban Scenes
Assia Benbihi, Cédric Pradalier, Ondřej Chum
We introduce Object-Guided Localization (OGuL) based on a novel method of local-feature matching. Direct matching of local features is sensitive to significant changes in illuminat…
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description
Assia Benbihi, Stéphanie Aravecchia, Matthieu Geist +1
Most of the research effort on image-based place recognition is designed for urban environments. In bucolic environments such as natural scenes with low texture and little semantic…
Learning Sensor Placement from Demonstration for UAV networks
Assia Benbihi, Matthieu Geist, Cédric Pradalier
This work demonstrates how to leverage previous network expert demonstrations of UAV deployment to automate the drones placement in civil applications. Optimal UAV placement is an…
ELF: Embedded Localisation of Features in pre-trained CNN
Assia Benbihi, Matthieu Geist, Cédric Pradalier
This paper introduces a novel feature detector based only on information embedded inside a CNN trained on standard tasks (e.g. classification). While previous works already show th…
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry
Xiaolong Wu, Assia Benbihi, Antoine Richard +1
Recent advances in deep learning for edge detection and segmentation opens up a new path for semantic-edge-based ego-motion estimation. In this work, we propose a robust monocular…
Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time
Assia Benbihi, Matthieu Geist, Cédric Pradalier
Deep learning generates state-of-the-art semantic segmentation provided that a large number of images together with pixel-wise annotations are available. To alleviate the expensive…