28 citations · 40 across the 12 of their papers we have counts for
13 papers
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
Ozan Unal, Dengxin Dai, Lukas Hoyer +2
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necess…
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
The local road network information is essential for autonomous navigation. This information is commonly obtained from offline HD-Maps in terms of lane graphs. However, the local ro…
Improving Online Lane Graph Extraction by Object-Lane Clustering
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a…
Online Lane Graph Extraction from Onboard Video
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous driving requires a structured understanding of the surrounding road network to navigate. One of the most common and useful representation of such an understanding is don…
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
We study the problem of estimating 3D shape and pose of an object in terms of keypoints, from a single 2D image. The shape and pose are learned directly from images collected by ca…
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
In this paper, we study the representation of the shape and pose of objects using their keypoints. Therefore, we propose an end-to-end method that simultaneously detects 2D keypoin…
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furtherm…
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the…
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
Yigit Baran Can, Alexander Liniger, Ozan Unal +2
Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, thi…
Towards Spectral Estimation from a Single RGB Image in the Wild
Berk Kaya, Yigit Baran Can, Radu Timofte
In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e.g. unk…
Learning to Segment Medical Images with Scribble-Supervision Alone
Yigit B. Can, Krishna Chaitanya, Basil Mustafa +3
Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation al…
An efficient CNN for spectral reconstruction from RGB images
Yigit Baran Can, Radu Timofte
Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al. Th…
Fast 3D Variable-FOV Reconstruction for Parallel Imaging with Localized Sensitivities
Yiğit Baran Can, Efe Ilıcak, Tolga Çukur
Several successful iterative approaches have recently been proposed for parallel-imaging reconstructions of variable-density (VD) acquisitions, but they often induce substantial co…