A Fast and Robust Framework for Semiautomatic and Automatic Registration of Photographs to 3D Geometry

Pintus, Ruggero; Gobbetti, Enrico

doi:10.1145/2629514

Cited by 17 publications

(15 citation statements)

References 64 publications

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“…Current registration software (Chen et al, 2008) and commercial tools demand correspondence definition in 3D, which is perceptually problematic to novice users (Brunnett et al, 2003), and therefore time-consuming, error-prone and inefficient. Correspondence-based methods are incorporated in complex algorithms (Pintus et al, 2011, Sottile et al, 2010, or serve as a comparison baseline.…”

Section: Manual Correspondence-based Methodsmentioning

confidence: 99%

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Kehl¹,

Buckley²,

Gawthorpe³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration-and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm's accuracy compared to traditional methods, and the impact of computational-and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

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Section: Manual Correspondence-based Methodsmentioning

confidence: 99%

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Kehl¹,

Buckley²,

Gawthorpe³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…If the worst point is better than the contracted point, the multicontraction is performed. At each step we check that the generated point is not outside the allowed reduced solution space (Pintus et al 2011). The pseudo code of the algorithm as follows: …”

Section: Parameters Optimizationmentioning

confidence: 99%

A Target Aware Texture Mapping for Sculpture Heritage Modeling

Yang

Zhang

Huang

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we proposed a target aware image to model registration method using silhouette as the matching clues. The target sculpture object in natural environment can be automatically detected from image with complex background with assistant of 3D geometric data. Then the silhouette can be automatically extracted and applied in image to model matching. Due to the user don't need to deliberately draw target area, the time consumption for precisely image to model matching operation can be greatly reduced. To enhance the function of this method, we also improved the silhouette matching algorithm to support conditional silhouette matching. Two experiments using a stone lion sculpture of Ming Dynasty and a potable relic in museum are given to evaluate the method we proposed. The method we proposed in this paper is extended and developed into a mature software applied in many culture heritage documentation projects.

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“…Our semi-automated method for registering 3D models to photographs is inspired by past work on registration [Franken et al 2005;Pintus et al 2011], architectural reconstruction [Wan et al 2012;Werner and Zisserman 2002], and automatic camera pose [Snavely et al 2008a;Snavely et al 2008b]. Such methods, including ours, are known as "incremental SfM" (adding one or a few photo(s) at time), and recent methods demonstrate improvements by solving the SfM problem at once [Crandall et al to appear].…”

Section: Related Workmentioning

confidence: 99%

ConstructAide

2014

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Figure 1: Our system aligns sets of photographs with 4D building models (a) to allow for new modes of construction-site interaction and visualization (using the ConstructAide GUI, b), such as architectural renderings (c), 4D navigation (d), and performance monitoring (e). AbstractWe describe a set of tools for analyzing, visualizing, and assessing architectural/construction progress with unordered photo collections and 3D building models. With our interface, a user guides the registration of the model in one of the images, and our system automatically computes the alignment for the rest of the photos using a novel Structure-from-Motion (SfM) technique; images with nearby viewpoints are also brought into alignment with each other. After aligning the photo(s) and model(s), our system allows a user, such as a project manager or facility owner, to explore the construction site seamlessly in time, monitor the progress of construction, assess errors and deviations, and create photorealistic architectural visualizations. These interactions are facilitated by automatic reasoning performed by our system: static and dynamic occlusions are removed automatically, rendering information is collected, and semantic selection tools help guide user input. We also demonstrate that our user-assisted SfM method outperforms existing techniques on both real-world construction data and established multiview datasets.

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A Fast and Robust Framework for Semiautomatic and Automatic Registration of Photographs to 3D Geometry

Cited by 17 publications

References 64 publications

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Direct Image-to-Geometry Registration Using Mobile Sensor Data

A Target Aware Texture Mapping for Sculpture Heritage Modeling

ConstructAide

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