Changjian Zhu

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate.

In this paper, we show an autocorrelation phase retrieval function (APRF) of light field to improve the rendering quality of novel views for light field rendering (LFR) technology. The phase of light field signal carries more abundant scene information, such as for scenes with occlusion, non-Lambertian, complex geometry and color texture. We apply the autocorrelation theory to convert the phase retrieval of the light field signal into the phase retrieval of the light field signal autocorrelation function, so as to facilitate the phase retrieval in the frequency domain. Therefore, we first derive the autocorrelation function of the light field signal and the influence of scene attributes. Based on the autocorrelation function of the light field, the phase retrieval of the light field signal is performed in the frequency domain for multiple iterations. By the phase retrieval optimization of the light field, the estimation and recovery of the scene information carried by the phase are realized, and the rendering quality of multi-view images is optimized. Additionally, to verify the effectiveness of this method, we compare the APRF with other reconstruction algorithms. The experimental results show the effectiveness of APRF in improving the reconstruction quality.

Occlusion is ubiquitous in the real world, and neglecting it will certainly lead to quality degeneration in a reconstructed light field (LF). In this paper, we derive a piecewise separation occlusion model (PSOM) for LF reconstruction that is optimized for occlusion in the continuous Fourier and integral domains. The PSOM is first obtained by determining the definite integral intervals between the occlusion and nonocclusion boundaries in the spatial domain along the camera direction. We then use these definite integral intervals to perform spectral analysis of the plenoptic function (POF) to derive the occluded and nonoccluded plenoptic spectra. Finally, we show that using the two sampling rates derived from the occluded and nonoccluded spectra is more effective than using a uniform sampling rate for reconstructing a single scene unless the scene is free of occlusion. Through numerical experiments on simulated signals and real-world inward-looking and outward-looking scenes, we demonstrate the effectiveness of our proposed approach.

Light field rendering is an effective multi-view reconstruction technology. The current multi-view reconstruction methods still has the problem of low view synthesis quality in complex scenes, such as occlusion and non Lambertian surfaces. In this article, we consider undistorted multiview reconstruction and develop a phase retrieval method for light field (PRLF) signals to improve the rendering quality. Our approach utilizes the phase spectrum in both the time domain and frequency domain of the discrete light field because the phase of the light field carries more scene information. The phase corrections are “light field truncation” in the time domain and “phase replacement” in the frequency domain. The iterative algorithm used provides high-quality estimated results for the relative phase of the light field. By the estimated phase results, we derive a multiview reconstruction method to improve the rendering quality. Finally, to verify the claimed performance, we also compare the PRLF with the most advanced light field reconstruction algorithms. The experimental results show that the proposed PRLF outperforms other known reconstruction schemes.