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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 …
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 …
Non-local spatial propagation network for depth completion
In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …
network for depth completion. The proposed network takes RGB and sparse depth images …
Bilateral propagation network for depth completion
Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …
Tri-perspective view decomposition for geometry-aware depth completion
Depth completion is a vital task for autonomous driving as it involves reconstructing the
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …
Dynamic spatial propagation network for depth completion
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 …
Evaluating scalable bayesian deep learning methods for robust computer vision
While deep neural networks have become the go-to approach in computer vision, the vast
majority of these models fail to properly capture the uncertainty inherent in their predictions …
majority of these models fail to properly capture the uncertainty inherent in their predictions …
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 …
Depth completion from sparse lidar data with depth-normal constraints
Depth completion aims to recover dense depth maps from sparse depth measurements. It is
of increasing importance for autonomous driving and draws increasing attention from the …
of increasing importance for autonomous driving and draws increasing attention from the …