Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
A survey of deep learning techniques for autonomous driving
The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
Towards semantic segmentation of urban-scale 3D point clouds: A dataset, benchmarks and challenges
An essential prerequisite for unleashing the potential of supervised deep learning
algorithms in the area of 3D scene understanding is the availability of large-scale and richly …
algorithms in the area of 3D scene understanding is the availability of large-scale and richly …
Badgr: An autonomous self-supervised learning-based navigation system
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's
objective is to perceive the geometry of the environment in order to plan collision-free paths …
objective is to perceive the geometry of the environment in order to plan collision-free paths …
Survey on semantic segmentation using deep learning techniques
Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …
have been developed to tackle this problem ranging from autonomous vehicles, human …
Deep multimodal fusion for semantic image segmentation: A survey
Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …
understanding tasks. However, in some complex environments or under challenging …
Efficientps: Efficient panoptic segmentation
Understanding the scene in which an autonomous robot operates is critical for its competent
functioning. Such scene comprehension necessitates recognizing instances of traffic …
functioning. Such scene comprehension necessitates recognizing instances of traffic …
Sparse and dense data with cnns: Depth completion and semantic segmentation
M Jaritz, R De Charette, E Wirbel… - … Conference on 3D …, 2018 - ieeexplore.ieee.org
Convolutional neural networks are designed for dense data, but vision data is often sparse
(stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth …
(stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth …
Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively
generates pseudo labels on unlabeled target data and retrains the network. However …
generates pseudo labels on unlabeled target data and retrains the network. However …