Probeless Illumination Estimation for Outdoor Augmented Reality

Madsen,; Lal,

doi:10.5772/7125

Cited by 16 publications

(15 citation statements)

References 10 publications

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“…This can be a diffuse sphere [3], a special shading probe [5] or only the user's hand [46]. Madsen and Lal [37] demonstrated a photometric reconstruction from shadows, Jachnik et al [24] used a specular surface. A first approach to augment live images based on geometry captured by a RGB-D camera was presented by Lensing and Broll [35].…”

Section: Previous Workmentioning

confidence: 99%

Interactive near-field illumination for photorealistic augmented reality on mobile devices

Rohmer

Büschel²,

Dachselt³

et al. 2014

2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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Mobile devices become more and more important today, especially for augmented reality (AR) applications in which the camera of the mobile device acts like a window into the mixed reality world. Up to now, no photorealistic augmentation is possible since the computational power of the mobile devices is still too weak. Even a streaming solution from a stationary PC would cause a latency that affects user interactions considerably. Therefore, we introduce a differential illumination method that allows for a consistent illumination of the inserted virtual objects on mobile devices, avoiding a delay. The necessary computation effort is shared between a stationary PC and the mobile devices to make use of the capacities available on both sides. The method is designed such that only a minimum amount of data has to be transferred asynchronously between the stationary PC and one or multiple mobile devices. This allows for an interactive illumination of virtual objects with a consistent appearance under both temporally and spatially varying real illumination conditions. To describe the complex near-field illumination in an indoor scenario, multiple HDR video cameras are used to capture the illumination from multiple directions. In this way, sources of illumination can be considered that are not directly visible to the mobile device because of occlusions and the limited field of view of built-in cameras.

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Section: Previous Workmentioning

confidence: 99%

Interactive near-field illumination for photorealistic augmented reality on mobile devices

Rohmer

Büschel²,

Dachselt³

et al. 2014

2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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“…[31]). All the above techniques mostly rely on a full 3D reconstruction or on dense 3D information [20]. In our approach, in order to lower down the overall complexity for online processing, we want to reduce the number of samples on which the computation will be done.…”

Section: Previous Workmentioning

confidence: 99%

“…When estimating illumination from every frame, the occlusion term s p is also required. It is difficult to accurately and consistently detect shadows under dynamically changing lighting: the most accurate techniques rely on the knowledge of 3D geometry [20,23]. Recent shadow detection techniques are also still quite expensive to reach a sufficiently accurate estimation [].…”

Section: Definition and Initialization Of Clustersmentioning

confidence: 99%

“…Indeed, the skylight is dominant in shadowed areas, and taking into account such values would improve the separation between sky and sun lighting. One solution for online estimation would be to use stereo cameras [20], but unfortunately, this provides the system with only partial 3D information about the visible scene, and shadows may be due to geometry not visible in the current viewpoint. As we are relying on a reduce set of feature points, future improvements need to deal with this partial 3D information.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Online Tracking of Outdoor Lighting Variations for Augmented Reality with Moving Cameras

Granier²

2012

IEEE Trans. Visual. Comput. Graphics

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“…Work that does not require a known geometrical object or second recording device usually assume an outdoor scene. Augmented reality in outdoor scenes is performed by [13,14] using a 3D sensor for realistic shadow rendering and GPS and compass information to determine the sun position. Other work for estimating the illumination conditions in outdoor scenes is performed by [9,11,12], where known properties of sunlight make this an easier problem to solve.…”

Section: Related Workmentioning

confidence: 99%

Point Light Source Estimation based on Scenes Recorded by a RGB-D camera

Boom

Orts-Escolano

Ning

et al. 2013

Procedings of the British Machine Vision Conference 2013

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Estimation of the point light source position in the scene enhances the experience for augmented reality. The intensity image and depth information from the RGB-D camera allows estimation of the point light source position in a scene, where our approach does not need any probe objects or other measuring devices. The approach uses the Lambertian reflectance model, where the RGB-D camera provides the image and the surface normals and the remaining unknowns are the albedo and light parameters (light intensity and direction). In order to determine the light parameters, we assume that segments with a similar colour have the same albedo, which allows us to find the point light source that explains the illumination in the scene. The performance of this method is evaluated on multiple scenes, where a single light bulb is used to illuminate the scene. In this case, the average error in the angle between the true light position vector and our estimate is around 10 degrees. This allows realistic rendering of synthetic objects into the recorded scene, which is used to improve the experience of augmented reality.

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Probeless Illumination Estimation for Outdoor Augmented Reality

Cited by 16 publications

References 10 publications

Interactive near-field illumination for photorealistic augmented reality on mobile devices

Interactive near-field illumination for photorealistic augmented reality on mobile devices

Online Tracking of Outdoor Lighting Variations for Augmented Reality with Moving Cameras

Point Light Source Estimation based on Scenes Recorded by a RGB-D camera

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