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Found 15 papers, 2 papers with code
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it.
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space.
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving
In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input.
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving
In this work, we aim to leverage the vehicle motion information and feed it into the model to have an adaptation mechanism based on ego-motion.
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks.
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline
It is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios to the best of our knowledge.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline
Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues.
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture.
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
We propose a novel GAN based algorithm for generating unseen patterns of soiled images.
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
The trajectory of the ego-vehicle is planned based on the future states of detected moving objects.
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors.
FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
In this work, we propose a CNN architecture for moving object detection using fisheye images that were captured in autonomous driving environment.
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation.
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications.
Optical Flow augmented Semantic Segmentation networks for Automated Driving
We also make use of the ground truth optical flow in Virtual KITTI to serve as an ideal estimator and a standard Farneback optical flow algorithm to study the effect of noise.
