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.
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.
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost.
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.
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving.
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)
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.