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Found 19 papers, 3 papers with code
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.
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving
Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency.
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches.
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.
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images.
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
To further illustrate the general applicability of the proposed framework, we apply it to wide-angle fisheye cameras with 190$^\circ$ horizontal field of view.
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset.
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view ($>180^{\circ}$).
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.
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics.
Ranked #6 on
3D Multi-Object Tracking
on KITTI
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks
For the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics.
Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors
High performance ultrasonic sensor hardware is mainly used in medical applications.
NeurAll: Towards a Unified Model for Visual Perception in Automated Driving
In this paper, we propose a joint multi-task network design for learning several tasks simultaneously.
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks
The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection.
Exploring Deep Spiking Neural Networks for Automated Driving Applications
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc.
Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation
The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
For instance, they do not capture the extensive variability in the appearance of objects like vehicles present in real datasets.
