148 citations · 169 across the 9 of their papers we have counts for
11 papers · 1 filter
Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
Stefanie Walz, Mario Bijelic, Andrea Ramazzina +3
We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captur…
STrack: Self-supervised Tracking with Soft Assignment Flow
Fatemeh Azimi, Fahim Mannan, Felix Heide
In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as lear…
The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object Detection
Geoffroi Côté, Fahim Mannan, Simon Thibault +2
Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other c…
Gated2Gated: Self-Supervised Depth Estimation from Gated Images
Amanpreet Walia, Stefanie Walz, Mario Bijelic +5
Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially…
Adversarial Imaging Pipelines
Buu Phan, Fahim Mannan, Felix Heide
Adversarial attacks play an essential role in understanding deep neural network predictions and improving their robustness. Existing attack methods aim to deceive convolutional neu…
Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues
Frank Julca-Aguilar, Jason Taylor, Mario Bijelic +3
Today's state-of-the-art methods for 3D object detection are based on lidar, stereo, or monocular cameras. Lidar-based methods achieve the best accuracy, but have a large footprint…
Neural Scene Graphs for Dynamic Scenes
Julian Ost, Fahim Mannan, Nils Thuerey +2
Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color…
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
Sai Rajeswar, Fahim Mannan, Florian Golemo +4
We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-explored, with most prior work relying on su…
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
Nicolas Scheiner, Florian Kraus, Fangyin Wei +8
Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (…
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
Mario Bijelic, Tobias Gruber, Fahim Mannan +4
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision…
Steady-state Non-Line-of-Sight Imaging
Wenzheng Chen, Simon Daneau, Fahim Mannan +1
Conventional intensity cameras recover objects in the direct line-of-sight of the camera, whereas occluded scene parts are considered lost in this process. Non-line-of-sight imagin…