# Lidar-Monocular Surface Reconstruction Using Line Segments

arXiv: https://arxiv.org/abs/2104.02761 · HTML: https://mlsanity.com/paper/2104.02761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth](https://mlsanity.com/paper/2003.05691/) (2020)
- [Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences](https://mlsanity.com/paper/2004.00740/) (2020)
- [LIMO: Lidar-Monocular Visual Odometry](https://mlsanity.com/paper/1807.07524/) (2018)
- [Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities](https://mlsanity.com/paper/1903.01067/) (2019)
- [LiDAR Enhanced Structure-from-Motion](https://mlsanity.com/paper/1911.03369/) (2019)
- [CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association](https://mlsanity.com/paper/1902.02256/) (2019)
- [A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions](https://mlsanity.com/paper/1907.00930/) (2019)

## Built on by (most cited first, 1 of 1)

- [LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues](https://mlsanity.com/paper/2410.22213/) (2024)

## Abstract (arXiv)

Structure from Motion (SfM) often fails to estimate accurate poses in environments that lack suitable visual features. In such cases, the quality of the final 3D mesh, which is contingent on the accuracy of those estimates, is reduced. One way to overcome this problem is to combine data from a monocular camera with that of a LIDAR. This allows fine details and texture to be captured while still accurately representing featureless subjects. However, fusing these two sensor modalities is challenging due to their fundamentally different characteristics. Rather than directly fusing image features and LIDAR points, we propose to leverage common geometric features that are detected in both the LIDAR scans and image data, allowing data from the two sensors to be processed in a higher-level space. In particular, we propose to find correspondences between 3D lines extracted from LIDAR scans and 2D lines detected in images before performing a bundle adjustment to refine poses. We also exploit the detected and optimized line segments to improve the quality of the final mesh. We test our approach on the recently published dataset, Newer College Dataset. We compare the accuracy and the completeness of the 3D mesh to a ground truth obtained with a survey-grade 3D scanner. We show that our method delivers results that are comparable to a state-of-the-art LIDAR survey while not requiring highly accurate ground truth pose estimates.