Depth Completion
67 papers with code • 9 benchmarks • 10 datasets
The Depth Completion task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.
Source: LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery , Unsupervised Depth Completion from Visual Inertial Odometry
Benchmarks
These leaderboards are used to track progress in Depth Completion
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Datasets
KITTI
NYUv2
Matterport3D
KITTI-Depth
VOID
TransCG
SuperCaustics
BIDCD
PLAD
Most implemented papers
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
fangchangma/sparse-to-dense.pytorch
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Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
XuelianCheng/LidarStereoNet
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CVPR 2019
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps.
Indoor Depth Completion with Boundary Consistency and Self-Attention
tsunghan-wu/depth-completion
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We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms.
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
fangchangma/self-supervised-depth-completion
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Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.
Unsupervised Depth Completion from Visual Inertial Odometry
alexklwong/unsupervised-depth-completion-visual-inertial-odometry
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Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points.
Learning Guided Convolutional Network for Depth Completion
It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation
VIS4ROB-lab/aerial-depth-completion
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In this paper, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources.
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
NVlabs/implicit_depth
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CVPR 2021
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
MMehdiMousavi/SuperCaustics
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In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.
