Image to Point Cloud Registration
9 papers with code • 1 benchmarks • 1 datasets
Given a query image and a scene of point cloud, get the camera pose according to them.
KITTI
Most implemented papers
3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair
The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches.
DeepI2P: Image-to-Point Cloud Registration via Deep Classification
This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud.
Learning general and distinctive 3D local deep descriptors for point cloud registration
fabiopoiesi/gedi
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An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains.
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
rsy6318/CorrI2P
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Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists of three modules, i. e., feature embedding, symmetric overlapping region detection, and pose estimation through the established correspondence.
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
They seek correspondences over downsampled superpoints, which are then propagated to dense points.
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization
minnjung/ep2p-loc
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ICCV 2023
Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map.
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration.
Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration
In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches.
Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
DavidBoja/exhaustive-grid-search
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Machine Vision and Applications 2024
The FP benchmark addresses the limitations of the current benchmarks: lack of data and parameter range variability, and allows to evaluate the strengths and weaknesses of a 3D registration method w. r. t.
