Rig-Aware 3D Reconstruction of Vehicle Undercarriages using Gaussian Splatting
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914267128659968 |
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| author | Kulkarni, Nitin Devarashetti, Akhil Cluss, Charlie Forte, Livio Buckmaster, Dan Schneider, Philip Qiao, Chunming Vereshchaka, Alina |
| author_facet | Kulkarni, Nitin Devarashetti, Akhil Cluss, Charlie Forte, Livio Buckmaster, Dan Schneider, Philip Qiao, Chunming Vereshchaka, Alina |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_14208 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rig-Aware 3D Reconstruction of Vehicle Undercarriages using Gaussian Splatting Kulkarni, Nitin Devarashetti, Akhil Cluss, Charlie Forte, Livio Buckmaster, Dan Schneider, Philip Qiao, Chunming Vereshchaka, Alina Computer Vision and Pattern Recognition Graphics Machine Learning 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. |
| title | Rig-Aware 3D Reconstruction of Vehicle Undercarriages using Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Graphics Machine Learning |
| url | https://arxiv.org/abs/2601.14208 |