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International audience; This paper proposes a novel framework to produce 3D, high-precision models of humans from multi-view capture. This method's inputs are a visual hull and several sets of multi-baseline views. For each such view set, a surface is reconstructed with a multi-baseline stereovision method, then used to carve the visual hull. Carved visual hulls from different view sets are then fused pairwise to deliver the intended 3D model. The contributions of this paper are threefold: (i) the addition of visual hull guidance to a multi-baseline stereovision method, (ii) a carving solution to a visual hull from an interpolated and smooth stereovision surface, and (iii) a fusion solution to merge differently carved volumes differing in several areas. The paper shows that the proposed approach helps recovering a high quality carved volume, a 3D representation of the human to be modelled, that is precise even for small details and in concave areas subjected to occlusion.

High dynamic range (HDR) imaging and stereoscopic 3D are active research fields, with challenges posed in various directions: acquisition, processing, and display. This chapter analyzes the latest advances in stereo HDR imaging acquisition from multiple exposures, in order to highlight the current progress toward a common target: stereoscopic 3D HDR video. A method to build multiscopic HDR images from low dynamic range (LDR) multiexposures is proposed. This method is based on a patch-match algorithm which has been adapted and improved to take advantage of epipolar geometry constraints of stereo images. Experimental results show accurate matching between stereo images that allow generating HDR for each LDR view of the sequence.

4D multi-view reconstruction of moving actors has many applications in the entertainment industry and although studios providing such services become more accessible, efforts have to be done in order to improve the underlying technology to produce high-quality 4D contents. In this paper, we present a method to derive a time-evolving surface representation from a sequence of binary volumetric data representing an arbitrary motion in order to introduce coherence in the data. The context is provided by an indoor multi-camera system which performs synchronized video captures from multiple viewpoints in a chroma-key studio. Our input is given by a volumetric silhouette-based reconstruction algorithm that generates a visual hull at each frame of the video sequence. These 3D volumetric models lack temporal coherence, in terms of structure and topology, as each frame is generated independently. This prevents an easy post-production editing with 3D animation tools. Our goal is to transform this input sequence of independent 3D volumes into a single dynamic structure, directly usable in post-production. Our approach is based on a motion estimation procedure. An unsigned distance function on the volumes is used as the main shape descriptor and a 3D surface matching algorithm minimizes the interference between unrelated surface regions. Experimental results, tested on our multi-view datasets, show that our method outperforms other approaches based on optical flow when considering robustness over several frames.

Relighting algorithms make it possible to take a model of a real-world scene and virtually modify its lighting. Unlike other methods that require controlled conditions, we introduce a new radiance capture method that allows the user to capture parts of the scene under different lighting conditions. A novel calibration method is presented that finds the positions of reflective spheres and their mathematically accurate projection onto the scene geometry. The resulting radiance distribution is used to estimate a diffuse reflectance for each object, computed coherently using the appropriate light probe image. Finally, the scene is relit using a novel illumination pattern.