67 citations · 111 across the 14 of their papers we have counts for
16 papers
Physics-Aware Diffusion for LiDAR Point Cloud Densification
Zeping Zhang, Robert Laganière
LiDAR perception is severely limited by the distance-dependent sparsity of distant objects. While diffusion models can recover dense geometry, they suffer from prohibitive latency…
mRadNet: A Compact Radar Object Detector with MetaFormer
Huaiyu Chen, Fahed Hassanat, Robert Laganiere +1
Frequency-modulated continuous wave radars have gained increasing popularity in the automotive industry. Their robustness against adverse weather conditions makes it a suitable cho…
Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity
Wassim El Ahmar, Dhanvin Kolhatkar, Farzan Nowruzi +1
Multiple Object Tracking (MOT) in thermal imaging presents unique challenges due to the lack of visual features and the complexity of motion patterns. This paper introduces an inno…
FSMDet: Vision-guided feature diffusion for fully sparse 3D detector
Tianran Liu, Morteza Mousa Pasandi, Robert Laganiere
Fully sparse 3D detection has attracted an increasing interest in the recent years. However, the sparsity of the features in these frameworks challenges the generation of proposals…
What You See Is What You Detect: Towards better Object Densification in 3D detection
Tianran Liu, Zeping Zhang, Morteza Mousa Pasandi +1
Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the…
T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals
James Giroux, Martin Bouchard, Robert Laganiere
Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks s…
RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users
Ao Zhang, Farzan Erlik Nowruzi, Robert Laganiere
Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public…
Point Cloud based Hierarchical Deep Odometry Estimation
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +1
Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using poin…
PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated d…
Deep Open Space Segmentation using Automotive Radar
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations c…
A Curious Link Between Prime Numbers, the Maundy Cake Problem and Parallel Sorting
Jonathan Blanchette, Robert Laganière
We present new theoretical algorithms that sums the n-ary comparators output in order to get the permutation indices in order to sort a sequence. By analysing the parallel ranking…
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar +3
In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requ…
On Batch Orthogonalization Layers
Blanchette, Laganière
Batch normalization has become ubiquitous in many state-of-the-art nets. It accelerates training and yields good performance results. However, there are various other alternatives…
Application of DenseNet in Camera Model Identification and Post-processing Detection
Abdul Muntakim Rafi, Uday Kamal, Rakibul Hoque +4
Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through we…
Multi-scale prediction for robust hand detection and classification
Ding Lu, Yong Wang, Robert Laganiere +2
In this paper, we present a multi-scale Fully Convolutional Networks (MSP-RFCN) to robustly detect and classify human hands under various challenging conditions. In our approach, t…
Gradient Boundary Histograms for Action Recognition
Feng Shi, Robert Laganiere, Emil Petriu
This paper introduces a high efficient local spatiotemporal descriptor, called gradient boundary histograms (GBH). The proposed GBH descriptor is built on simple spatio-temporal gr…