A review of vision-based traffic semantic understanding in ITSs
J Chen, Q Wang, HH Cheng, W Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …
situations and emergencies more accurately and provide a more accurate basis for anomaly …
Deep learning for image and point cloud fusion in autonomous driving: A review
Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
Penet: Towards precise and efficient image guided depth completion
Image guided depth completion is the task of generating a dense depth map from a sparse
depth map and a high quality image. In this task, how to fuse the color and depth modalities …
depth map and a high quality image. In this task, how to fuse the color and depth modalities …
Dynamic spatial propagation network for depth completion
Y Lin, T Cheng, Q Zhong, W Zhou… - Proceedings of the aaai …, 2022 - ojs.aaai.org
Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …
measurements and corresponding RGB images. Currently, spatial propagation networks …
Completionformer: Depth completion with convolutions and vision transformers
Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …
propagating the sparse measurements throughout the whole image to get a dense depth …
RigNet: Repetitive image guided network for depth completion
Depth completion deals with the problem of recovering dense depth maps from sparse ones,
where color images are often used to facilitate this task. Recent approaches mainly focus on …
where color images are often used to facilitate this task. Recent approaches mainly focus on …
Learning guided convolutional network for depth completion
Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …
Adaptive context-aware multi-modal network for depth completion
Depth completion aims to recover a dense depth map from the sparse depth data and the
corresponding single RGB image. The observed pixels provide the significant guidance for …
corresponding single RGB image. The observed pixels provide the significant guidance for …
Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion
Depth Completion deals with the problem of converting a sparse depth map to a dense one,
given the corresponding color image. Convolutional spatial propagation network (CSPN) is …
given the corresponding color image. Convolutional spatial propagation network (CSPN) is …
Deep depth completion from extremely sparse data: A survey
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …