150 citations · 211 across the 18 of their papers we have counts for
20 papers
A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras
Runze Yuan, Alexander Kappler, Jun Zhang +5
In many autonomous applications requiring real-time localization, active marker-based systems are preferred due to their low latency and ease of deployment compared to computationa…
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
Quentin Herau, Nathan Piasco, Moussab Bennehar +6
Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and e…
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
Quentin Herau, Moussab Bennehar, Arthur Moreau +6
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have…
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To corr…
Alignment-free HDR Deghosting with Semantics Consistent Transformer
Steven Tel, Zongwei Wu, Yulun Zhang +4
High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual informa…
Object Segmentation by Mining Cross-Modal Semantics
Zongwei Wu, Jingjing Wang, Zhuyun Zhou +5
Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy.…
Mobile Mapping Mesh Change Detection and Update
Teng Wu, Bruno Vallet, Cédric Demonceaux
Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The…
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the informat…
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau +2
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods e…
Source-free Depth for Object Pop-out
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan +5
Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promi…
RGB-Event Fusion for Moving Object Detection in Autonomous Driving
Zhuyun Zhou, Zongwei Wu, Rémi Boutteau +3
Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approa…
Robust RGB-D Fusion for Saliency Detection
Zongwei Wu, Shriarulmozhivarman Gobichettipalayam, Brahim Tamadazte +3
Efficiently exploiting multi-modal inputs for accurate RGB-D saliency detection is a topic of high interest. Most existing works leverage cross-modal interactions to fuse the two s…
Depth-Adapted CNNs for RGB-D Semantic Segmentation
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side. Existing works often adopt a two-str…
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks
Daniel Braun, Olivier Morel, Pascal Vasseur +1
Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap…
Modality-Guided Subnetwork for Salient Object Detection
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the…
Depth-Adapted CNN for RGB-D cameras
Zongwei Wu, Guillaume Allibert, Christophe Stolz +1
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the ph…
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets
Clara Fernandez-Labrador, Ajad Chhatkuli, Danda Pani Paudel +3
Automatic discovery of category-specific 3D keypoints from a collection of objects of some category is a challenging problem. One reason is that not all objects in a category neces…
What's in my Room? Object Recognition on Indoor Panoramic Images
Julia Guerrero-Viu, Clara Fernandez-Labrador, Cédric Demonceaux +1
In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several task…
Corners for Layout: End-to-End Layout Recovery from 360 Images
Clara Fernandez-Labrador, Jose M. Facil, Alejandro Perez-Yus +3
The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among t…
PanoRoom: From the Sphere to the 3D Layout
Clara Fernandez-Labrador, Jose M. Facil, Alejandro Perez-Yus +2
We propose a novel FCN able to work with omnidirectional images that outputs accurate probability maps representing the main structure of indoor scenes, which is able to generalize…