1.4k citations · 2.6k across the 155 of their papers we have counts for
21 papers · 2 filters
Surface Normal Clustering for Implicit Representation of Manhattan Scenes
Nikola Popovic, Danda Pani Paudel, Luc Van Gool
Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to…
ManiFlow: Implicitly Representing Manifolds with Normalizing Flows
Janis Postels, Martin Danelljan, Luc Van Gool +1
Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. However, their invertibility constra…
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha +7
One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing b…
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
David Bruggemann, Christos Sakaridis, Prune Truong +1
Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UD…
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
Kevin Ta, David Bruggemann, Tim Brödermann +2
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged…
Gradient Obfuscation Checklist Test Gives a False Sense of Security
Nikola Popovic, Danda Pani Paudel, Thomas Probst +1
One popular group of defense techniques against adversarial attacks is based on injecting stochastic noise into the network. The main source of robustness of such stochastic defens…
HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection
Tim Broedermann, Christos Sakaridis, Dengxin Dai +1
Besides standard cameras, autonomous vehicles typically include multiple additional sensors, such as lidars and radars, which help acquire richer information for perceiving the con…
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger +1
Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time.…
FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty
Liqian Ma, Stamatios Georgoulis, Xu Jia +1
The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and ro…
Direct Dense Pose Estimation
Liqian Ma, Lingjie Liu, Christian Theobalt +1
Dense human pose estimation is the problem of learning dense correspondences between RGB images and the surfaces of human bodies, which finds various applications, such as human bo…
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further…
Neural Vector Fields for Implicit Surface Representation and Inference
Edoardo Mello Rella, Ajad Chhatkuli, Ender Konukoglu +1
Implicit fields have recently shown increasing success in representing and learning 3D shapes accurately. Signed distance fields and occupancy fields are decades old and still the…
Learning Local and Global Temporal Contexts for Video Semantic Segmentation
Guolei Sun, Yun Liu, Henghui Ding +2
Contextual information plays a core role for video semantic segmentation (VSS). This paper summarizes contexts for VSS in two-fold: local temporal contexts (LTC) which define the c…
Transforming Model Prediction for Tracking
Christoph Mayer, Martin Danelljan, Goutam Bhat +4
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective fun…
Spatially Multi-conditional Image Generation
Ritika Chakraborty, Nikola Popovic, Danda Pani Paudel +2
In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple ta…
Robust Visual Tracking by Segmentation
Matthieu Paul, Martin Danelljan, Christoph Mayer +1
Estimating the target extent poses a fundamental challenge in visual object tracking. Typically, trackers are box-centric and fully rely on a bounding box to define the target in t…
Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild
Ardhendu Shekhar Tripathi, Martin Danelljan, Samarth Shukla +2
We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments betwe…
Zero Pixel Directional Boundary by Vector Transform
Edoardo Mello Rella, Ajad Chhatkuli, Yun Liu +2
Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically l…
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
Prune Truong, Martin Danelljan, Fisher Yu +1
We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching. Our approach directly supervises the dense matching scores predicted by the…
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
Menelaos Kanakis, Simon Maurer, Matteo Spallanzani +2
Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping. Such systems still rely on traditional hand-cra…
Sound and Visual Representation Learning with Multiple Pretraining Tasks
Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool
Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In th…