![]() The limited FOV would only capture about 6% of the panoramic scene according to . The illumination of a scene is resulted in by many factors, including various lighting sources, surface reflectance, scene geometry, and object inter-reflections. ![]() It is mainly due to the complexity of HDR environment maps and the missing information from the input LDR RGB image. Although the learning-based works achieve plausible results, recovering the illumination of the entire scene is still a highly ill-posed problem. Prior works make use of the sky model and try to infer the outdoor lighting in . Works are reported to recover HDR environment maps from a single limited field-of-view (FOV) low dynamic range (LDR) image for indoor scenes . Therefore, recent works are reported to infer HDR environment maps from limited input information by learning. Although these reported methods work well in certain scenarios, such requirements would not always be feasible for most of practical applications. Some works assume that additional information is available, e.g., panoramas , depth , or user input . Some works are reported to insert certain objects into scenes , such as light probes , 3D objects with known properties, or human faces . In order to achieve realistic rendering, prior works in the literature try to obtain the HDR environment maps in various ways. But these techniques only consider the camera exposure information and are regarded to be rudimentary. On the contrary, commercial AR tools, e.g., Google’s ARCore or Apple’s ARkit, provide lightweight mobile applications to estimate scene illuminations. However, direct capture of HDR images is not feasible for most cases, as it requires tedious setups and expensive devices . It reproduces a great dynamic range of illumination which is even higher than that of the human visual system. The high dynamic range (HDR) environment maps are usually adopted to record the illumination of the entire scene. Conventionally, the problem consists of two steps, i.e., lighting estimation and virtual object rendering. Emerging applications, such as augmented reality (AR), mixed reality (MR), live streaming, or film production, demand realistic graphical visualization and rendering . It shows the effectiveness and robustness for realistic virtual object insertion and improved realism.Ĭompositing realistic virtual objects rendered into real scenes and illumination estimation is fundamental, but challenging problems in computer vision and computer graphics. It is observed that notable experiment results and comparison outcomes have been obtained quantitatively and qualitatively by the proposed algorithm in different environments. Compared to previous works in the literature, the proposed algorithm is more robust, as it is able to efficiently recover spatially varying illumination in both indoor and outdoor scene environments. A generative adversarial network is adopted in the proposed algorithm for implicit illumination features extraction and transferring. ![]() It is achieved by transferring implicit illumination features which are extracted from its nearby planar surfaces. Given a single low dynamic range image, instead of recovering lighting environment of the entire scene, the proposed algorithm directly infers the relit virtual object. In this paper, an object-based illumination transferring and rendering algorithm is proposed to tackle this problem within a unified framework. Prior learning-based methods reported in the literature usually attempt to reconstruct complicated high dynamic range environment maps from limited input, and rely on a separate rendering pipeline to light up the virtual object. ![]() In applications of augmented reality or mixed reality, rendering virtual objects in real scenes with consistent illumination is crucial for realistic visualization experiences. ![]()
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