J Hu, C Bao, M Ozay, C Fan, Q Gao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
M Hu, S Wang, B Li, S Ning, L Fan… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
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 …
Abstract 3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this …
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 …
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 …
S Zhao, M Gong, H Fu, D Tao - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
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 …
X Cheng, P Wang, C Guan, R Yang - … of the AAAI conference on artificial …, 2020 - aaai.org
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 …
Y Wang, B Li, G Zhang, Q Liu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data. Although such approaches …