PixLoc

Introduced by Sarlin et al. in Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

PixLoc is a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. It is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. As the CNN never sees 3D points, PixLoc can generalize to any 3D structure available. This includes sparse SfM point clouds, dense depth maps from stereo or RGBD sensors, meshes, Lidar scans, but also lines and other primitives.

Source: Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Camera Localization	1	33.33%
Metric Learning	1	33.33%
Pose Estimation	1	33.33%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
🤖 No Components Found	You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories

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6D Pose Estimation Models