Lidar-Monocular Surface Reconstruction Using Line Segments

TL;DR

This paper introduces a novel method for surface reconstruction that combines LIDAR and monocular camera data by leveraging shared geometric line features, improving 3D mesh quality without needing precise pose estimates.

Contribution

The paper proposes a new approach to fuse LIDAR and monocular camera data using line correspondences and bundle adjustment, enhancing 3D reconstruction accuracy and detail.

Findings

Achieves comparable accuracy to survey-grade LIDAR scans

Improves mesh completeness and detail

Does not require highly accurate pose estimates

Abstract

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.