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Found 8 papers, 0 papers with code
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
VideoClick: Video Object Segmentation with a Single Click
Towards this goal, in this paper we propose a bottom up approach where given a single click for each object in a video, we obtain the segmentation masks of these objects in the full video.
DAGMapper: Learning to Map by Discovering Lane Topology
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost.
End-to-End Deep Structured Models for Drawing Crosswalks
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery.
Convolutional Recurrent Network for Road Boundary Extraction
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely.
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving.
PolyTransform: Deep Polygon Transformer for Instance Segmentation
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods.
Ranked #1 on
Instance Segmentation
on Cityscapes test
(using extra training data)
TorontoCity: Seeing the World with a Million Eyes
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
