Keypoint-Guided NeRF Patches for Outdoor Visual Relocalization
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Solution Overview
Problem
Existing visual relocalization methods face inefficiencies due to high storage requirements and poor accuracy, particularly in outdoor scenes, with Neural Radiance Field (NeRF) methods being slow and inefficient for rendering complete images.
Innovation Solution
A method combining an explicit 3D representation with an implicit 3D representation using neural radiance fields, rendering only sparse patches around valid keypoints to reduce computation time and improve accuracy, leveraging clustering for large-scale scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NeRF is used for visual relocalization, then accuracy is improved, but rendering time increases significantly
Solution Approach 1:
The patent divides the scene into multiple patches and processes only relevant patches instead of rendering the entire scene. This segmentation approach maintains accuracy by focusing computational resources on key regions while dramatically reducing overall rendering time.
Solution Approach 2:
The patent applies partial action by rendering only necessary patches rather than complete images. This selective rendering approach provides sufficient information for accurate pose estimation without the excessive computational cost of full-scene rendering.
2Measurement precision
If hierarchical methods are used, then accuracy is improved, but storage requirements increase due to large number of text descriptors
Solution Approach 1:
The patent extracts only the essential features needed for relocalization by using 3D Gaussian splatting to represent scene geometry and appearance. This extraction approach eliminates the need to store large numbers of text descriptors while preserving the information necessary for accurate pose estimation.
Solution Approach 2:
The patent transitions from 2D image-space descriptors to a 3D scene representation using Gaussian splatting. This dimensional change allows the system to store scene information more efficiently in a compact 3D format rather than requiring extensive 2D descriptor storage.
3Productivity
If APR and SCR methods are used, then efficiency is improved, but accuracy deteriorates especially in outdoor scenes
Solution Approach 1:
The patent combines the efficiency of direct regression methods with the accuracy of NeRF by integrating 3D Gaussian splatting with pose regression. This composite approach merges the advantages of both paradigms, achieving high computational efficiency while maintaining accurate pose estimation in outdoor scenes.
Data Source
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AI summary
A computer-implemented method for visual relocalization task is provided. The method comprises: (S10) generating an explicit 3D representation of a scene based on reference images of the scene taken from reference poses, each reference image comprising a global descriptor and valid keypoints having corresponding elements in the explicit 3D representation; (S20) learning an implicit 3D representation of the scene based on a neural radiance field model; (S30) determining candidate images matching the query image by comparing global descriptors of the reference images to a global descriptor of the query image; (S40) rendering a plurality of patches of the candidate images using the implicit 3D representation, the patches reflecting zones around the valid keypoints of the explicit 3D representation viewed from poses of candidate images; and (S50) determining the pose of the query image by matching local descriptors extracted from the rendered patches to local keypoints of the query image.