Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features

2024-12-08

arXiv.org

AI Summary

In 3D reconstruction, geometrically distinct yet visually similar surfaces—termed *doppelgängers*—induce erroneous feature correspondences, degrading Structure-from-Motion (SfM) accuracy and causing model distortions. To address this cross-domain challenge, we propose a doppelgänger detection framework: (1) a geographically diverse, GPS-tagged training dataset; (2) a Transformer-based classifier leveraging 3D features extracted by MASt3R; and (3) an automated geographic label verification mechanism to enhance annotation reliability. Our method tightly integrates SfM and MASt3R-SfM pipelines, enabling joint geometric-semantic discrimination. Experiments demonstrate substantial improvements in both precision and recall for false-match detection, significantly enhancing reconstruction fidelity and robustness in complex real-world scenes. The approach provides a scalable, verifiable solution to visual ambiguity in large-scale 3D reconstruction.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious matches distort the structure-from-motion (SfM) process, leading to misplaced model elements and reduced accuracy. Prior efforts addressed this with CNN classifiers trained on curated datasets, but these approaches struggle to generalize across diverse real-world scenes and can require extensive parameter tuning. In this work, we present Doppelgangers++, a method to enhance doppelganger detection and improve 3D reconstruction accuracy. Our contributions include a diversified training dataset that incorporates geo-tagged images from everyday scenes to expand robustness beyond landmark-based datasets. We further propose a Transformer-based classifier that leverages 3D-aware features from the MASt3R model, achieving superior precision and recall across both in-domain and out-of-domain tests. Doppelgangers++ integrates seamlessly into standard SfM and MASt3R-SfM pipelines, offering efficiency and adaptability across varied scenes. To evaluate SfM accuracy, we introduce an automated, geotag-based method for validating reconstructed models, eliminating the need for manual inspection. Through extensive experiments, we demonstrate that Doppelgangers++ significantly enhances pairwise visual disambiguation and improves 3D reconstruction quality in complex and diverse scenarios.

Problem

• Improves visual disambiguation in 3D reconstruction
• Reduces spurious matches in structure-from-motion
• Enhances generalization across diverse real-world scenes

Innovation

• Transformer-based classifier with 3D-aware features
• Diversified training dataset from geo-tagged images
• Automated geotag-based SfM validation method