5 papers · 1 filter
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis +1
Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains…
SCAN: Learning to Classify Images without Labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis +2
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important…
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video
Vaishakh Patil, Wouter Van Gansbeke, Dengxin Dai +1
Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video in…
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
Wouter Van Gansbeke, Davy Neven, Bert De Brabandere +1
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to ac…
End-to-end Lane Detection through Differentiable Least-Squares Fitting
Wouter Van Gansbeke, Bert De Brabandere, Davy Neven +2
Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline)…