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Scalable and Generalizable Correspondence Pruning Via Geometry-Consistent Pre-Training.

Tangfei Liao, Xiaoqin Zhang, Tao Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 10, 2026

Summary

This summary is machine-generated.

We introduce GeneralPruner, a novel geometry-consistent pre-training method that significantly improves 3D vision tasks by learning robust representations for camera pose estimation and more, free from outlier interference.

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Area of Science:

Computer Vision
Machine Learning
3D Reconstruction

Background:

Two-view correspondence pruning is crucial for camera pose estimation in 3D vision.
Existing methods struggle with outlier correspondences, hindering representation learning.
Outliers negatively impact model robustness and generalizability.

Purpose of the Study:

To develop a geometry-consistent pre-training paradigm for scalable and generalizable representations.
To overcome the limitations of outlier interference in correspondence pruning.
To enhance the performance of downstream 3D vision tasks.

Main Methods:

Introduced masked inlier reconstruction as a pretext task within a masked autoencoder framework.
Employed a dual-branch structure for indirect reconstruction of 4D correspondences.
Proposed a unified dual-stream encoder with built-in consensus interaction.

Main Results:

GeneralPruner outperforms state-of-the-art methods in robustness and generalization.
Achieved significant performance gains: 10.76% in camera pose estimation, 11.84% in visual localization, and 8.65% in 3D registration.
Demonstrated the effectiveness of the geometry-consistent pre-training paradigm.

Conclusions:

The proposed pre-training framework offers a universal and scalable solution for correspondence pruning.
GeneralPruner effectively learns representations free from outlier interference.
This work pioneers pre-training for correspondence pruning, advancing 3D vision capabilities.