Rig-Aware 3D Reconstruction of Vehicle Undercarriages using Gaussian Splatting

2026-01-20

AI Summary

This work proposes an end-to-end pipeline to address the inefficiency and lack of online visualization in traditional manual vehicle undercarriage inspection. By mounting a rigid three-camera rig on a moving vehicle, synchronized video sequences are captured and processed through a structure-from-motion (SfM) framework tailored for rigid multi-view systems, incorporating precise calibration and geometric priors to effectively handle wide-angle lens distortion and low parallax. High-quality sparse point clouds are generated using DISK features matched with LightGlue attention mechanisms, which then initialize 3D Gaussian Splatting for real-time rendering. The resulting high-fidelity, interactive undercarriage model supports rotation, zooming, and cross-sectional viewing, significantly enhancing both damage detection efficiency and visualization quality, achieving state-of-the-art reconstruction performance.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Inspecting the undercarriage of used vehicles is a labor-intensive task that requires inspectors to crouch or crawl underneath each vehicle to thoroughly examine it. Additionally, online buyers rarely see undercarriage photos. We present an end-to-end pipeline that utilizes a three-camera rig to capture videos of the undercarriage as the vehicle drives over it, and produces an interactive 3D model of the undercarriage. The 3D model enables inspectors and customers to rotate, zoom, and slice through the undercarriage, allowing them to detect rust, leaks, or impact damage in seconds, thereby improving both workplace safety and buyer confidence. Our primary contribution is a rig-aware Structure-from-Motion (SfM) pipeline specifically designed to overcome the challenges of wide-angle lens distortion and low-parallax scenes. Our method overcomes the challenges of wide-angle lens distortion and low-parallax scenes by integrating precise camera calibration, synchronized video streams, and strong geometric priors from the camera rig. We use a constrained matching strategy with learned components, the DISK feature extractor, and the attention-based LightGlue matcher to generate high-quality sparse point clouds that are often unattainable with standard SfM pipelines. These point clouds seed the Gaussian splatting process to generate photorealistic undercarriage models that render in real-time. Our experiments and ablation studies demonstrate that our design choices are essential to achieve state-of-the-art quality.

Problem

• vehicle undercarriage
• 3D reconstruction
• inspection
• online buying
• rust detection

Innovation

• Rig-Aware SfM
• Gaussian Splatting
• Wide-Angle Distortion Correction
• Low-Parallax Reconstruction
• Constrained Feature Matching