Object-Level Priors for Stixel Generation
@inproceedings{Cordts2014ObjectLevelPF, title={Object-Level Priors for Stixel Generation}, author={Marius Cordts and L. Schneider and M. Enzweiler and Uwe Franke and S. Roth}, booktitle={GCPR}, year={2014} }
This paper presents a stereo vision-based scene model for traffic scenarios. [...] Key Method We present a principled way to additionally integrate top-down prior information about object location and shape that arises from independent system modules, ranging from geometric cues up to highly confident object detections. This results in an efficient exploration of orthogonal image-based cues, such as disparity and gray-level intensity data, combined in a consistent scene representation. The overall segmentation…Expand Abstract
18 Citations
Semantic Stixels: Depth is not enough
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- 2016 IEEE Intelligent Vehicles Symposium (IV)
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Slanted Stixels: A Way to Represent Steep Streets
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Stixel optimization: Representing challenging on-road scenes
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Low-level fusion of color, texture and depth for robust road scene understanding
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Use of a Confidence Map Towards Improved Multi-layer Stixel Segmentation
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Monocular Stixels: A LIDAR-guided Approach
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Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks
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The Stixel World: A medium-level representation of traffic scenes
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Instance Stixels: Segmenting and Grouping Stixels into Objects
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