Data-driving Algorithms for 3D Reconstruction from Ladar Data. Gerard Berginc1, Ion Berechet2, and Stefan Berechet2. 1Thales Optronique S.A., 2 Avenue Gay ...
PIERS Proceedings, Moscow, Russia, August 19–23, 2012
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Data-driving Algorithms for 3D Reconstruction from Ladar Data Gerard Berginc1 , Ion Berechet2 , and Stefan Berechet2 1
Thales Optronique S.A., 2 Avenue Gay Lussac, Elancourt Cedex 78995, France 2 SISPIA SARL, 18 All´ee Henri Dunant, Vincennes 94300, France
Abstract— There is a considerable interest in the development of new optical imaging systems that are able to give three-dimensional images. In this paper, we present some considerations concerning the field of three-dimensional laser images where significant technological advances have encouraged research over the past decade. Potential applications range across medical imaging, surveillance and robotic vision. Identifying targets or objects concealed by foliage or camouflage is a critical requirement for operations in public safety, law enforcement and defense. 1. INTRODUCTION
Laser radar (Ladar) technology has enjoyed significant advances over the past decade. Novel focal plane areas, compact laser illuminators and advanced signal processing have enabled the construction of low power 2-D and 3-D laser imagery systems. The applications of such systems range from surveillance, targeting and weapons guidance to target identification. Synthetic images of three-dimensional objects are based on extraction of laser backscattered signals [1]. The principle of 3D laser radar is based on the use of movable light sources and detectors to collect information on laser scattering, and to reconstruct the 3D object. 3D reconstruction algorithm is a major component in these optical systems for identification of camouflaged objects. But 3D reconstruction must take into account sparse collected data, i.e., concealed objects and reconstruction algorithms must solve a complex multi-parameter inverse problem. Therefore the inverse problem of recovering the surface three-dimensional shape function from intensity data is more challenging [2, 3]. The robustness of identification of three-dimensional reconstructed images is directly related to the inversion algorithms used in the process of identification. From a mathematical point of view, the technique breaks down into two steps: direct measurement, optionally processed using a model of the physical phenomena which are measured or in our case measured data, and then reconstruction by inversion on the basis of these direct measures. Artifacts from the reconstruction algorithms degrade the quality of identification and the object recognition. A notable limitation of numerous methods is that inversion algorithms produce sparse, blurred and noisy three-dimensional images. Therefore, the strategy of inversion must be optimized. The objective of our paper is to present a new data-driving algorithmic approach for the generation of 3D surface data from sparse 3D point clouds corresponding to the reconstruction algorithm. The role of this type of algorithmic data-driving process is to complete the missing parts of the 3D image at satisfactory levels for reliable identification of concealed objects. In this paper, we present different examples of reconstruction and completion of three-dimensional images. The data used in this paper come from simulations [4–6] that are based on the calculation of the laser interactions with the different interfaces of the scene of interest. Common identification algorithms use reference databases therefore identification of unknown objects which are not included in the knowledge database of objects becomes difficult. 2. DATA-DRIVING ALGORITHMS FOR 3D RECONSTRUCTION BASED ON 3D POINT CLOUDS ENERGY
In this section, a simulated scene reconstruction is achieved to evaluate our data-driving algorithm. We simulate a complete scene containing a hidden vehicle behind a canopy. The modeling (Fig. 1) includes the physical structure of the environment, the transfer of radiation through the environment, and the interaction of the laser wave with the structure of the different elements of the scene. The results of our models have been validated against real data for a range of sensor systems [4–6]. These models incorporate a detailed understanding of the interaction of the electromagnetic wave. We may obtain the three-dimensional reconstruction by a cone-beam algorithm [2, 4–6], which is a convolution back-projection algorithm deduced from the Radon transform. This algorithm uses a set of two-dimensional projections which contain the data collected by the pixels of a focal plane area. These data are related to the intensity backscattered by the scene illuminated by a laser
Progress In Electromagnetics Research Symposium Proceedings, Moscow, Russia, August 19–23, 2012 497
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Figure 1: The vehicle is hidden under foliage and the Ladar system, mounted on a moving platform for a air-to-ground scenario collects a set of 3D laser images of the scene from several aspects.
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Figure 2: Example of sparse 3D reconstruction: (a) isodensity and (b) 3D point clouds.
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Figure 3: Generated 3D surfaces in sparse 3D point clouds using 3D Data driving algorithms: (a) lateral side of vehicle, and (b) complete vehicle.
pulse. The scene is flood-illuminated with a single laser pulse (1540 nm), the eye-safe property of wavelengths around 1500 nm is perfectly suited to active laser imagery applications. A lateral view of the scene is presented in Fig. 1. A 3D laser image is then reconstructed (Fig. 2). A vehicle of interest is viewed through a dense scattering medium, in our case a mass of foliage. Since the foliage blocks almost all the laser pulse to the target, the 3D points on the target surface are sparse. The 3D reconstruction presents some incomplete areas in the 3D point cloud. Therefore it may be difficult for observers to recognize objects from a point cloud and we must enhance visual exploitation of 3D imaging Ladar data. The implementation of a data-driving algorithmic process [1, 2] can help to fill data in the incomplete areas and generate the object surface. This algorithmic process is based on partition
PIERS Proceedings, Moscow, Russia, August 19–23, 2012
498
of initial incomplete point clouds in significant areas by integration of cloud minimum energy, completion of incomplete significant areas and surface generation using an MLP approach with sensibility calculation for higher capacity of generalization and fusion of partially generated surfaces. The results of this data-driving algorithmic process are not dependent on other external elements only the incomplete three-dimensional point clouds being used (Figs. 3 and 4).
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Figure 4: Surfaces rendering using 3D Data driving algorithms: (a) vehicle front view, and (b) vehicle back view.
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Figure 5: Reconstructed 3D point clouds in turbulence: (a) complete vehicle, and (b) vehicle upper side.
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Figure 6: Generated 3D surfaces with Gaussian noise for the upper side of the vehicle: (a) generated surfaces and reconstructed point clouds, (b) generated surfaces.
Progress In Electromagnetics Research Symposium Proceedings, Moscow, Russia, August 19–23, 2012 499 3. NOISE IMPACT ON 3D RECONSTRUCTION USING DATA-DRIVING ALGORITHMS
We have analyzed the 3D reconstruction with added Gaussian noise defined by its standard deviation σ. This Gaussian noise is an approximation of the different noises we can encounter in the Ladar system (speckle, detector noise). We can notice that the reconstruction algorithm is robust and gives well-defined generated surfaces (Figs. 5 and 6). 4. CONCLUSIONS
We have demonstrated new algorithmic approaches to enhance visual exploitation of 3D imaging Ladar data. We have tested the robustness of the algorithm in cases of noisy scenarios. With these reconstruction procedures, we can separate objects from foliage and reconstruct a three-dimensional image of the considered object. ACKNOWLEDGMENT
This work is sponsored by the French Ministry of Economy, Industry and Employment (Directorate General of Competitiveness, Industry and Services). This project is part of program RAPID implemented by French Directorate General of Armament. REFERENCES
1. Berginc, G., I. Berechet, and S. Berechet, “Method for three-dimensional synthetic reconstruction of objects exposed to an electromagnetic and/or elastic wave,” US patent 2011/0019906 A1, Jan. 27, 2011. 2. Berechet, I. and G. Berginc, “Advanced algorithms for identifying targets from a threedimensional reconstruction of sparse 3D Ladar data,” Proceedings of SPIE, Vol. 8172, 81720Z, Optical Complex Systems, 2011. 3. Marino, M. and W. R. Davis, Jr., “Jigsaw: A foliage penetrating 3D imaging laser radar system,” Lincoln Laboratory Journal, Vol. 15, No. 1, 23–36, 2005. 4. Berginc, G. and M. Jouffroy, “Simulation of 3D laser systems,” Proceedings of the 2009 IEEE International Geoscience & Remote Sensing Symposium, Cape Town, South Africa, 440–444, 2009. 5. Berginc, G. and M. Jouffroy, “Simulation of 3D laser imaging,” PIERS Online, Vol. 6, No. 5, 415–419, 2010. 6. Berginc, G. and M. Jouffroy, “3D laser imaging,” PIERS Online, Vol. 7, No. 5, 411–415, 2011.
