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Found 14 papers, 4 papers with code
Learning Stixel-based Instance Segmentation
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation.
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time
In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images.
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
Object detection is an important task in environment perception for autonomous driving.
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work.
Slanted Stixels: A way to represent steep streets
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information.
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Ranked #16 on
Depth Completion
on KITTI Depth Completion
Slanted Stixels: Representing San Francisco's Steepest Streets
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information.
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
The Stixel world: A medium-level representation of traffic scenes
We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene.
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery.
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphical models.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
