Matis Hudon

Shape and Reflectance from RGB-D Images using Time Sequential Illumination

In this paper we propose a method for recovering the shape (geometry) and the diffuse reflectance from an image (or video) using a hybrid setup consisting of a depth sensor (Kinect), a consumer camera and a partially controlled illumination (using a flash). The objective is to show how combining RGB-D acquisition with a sequential illumination is useful for shape and reflectance recovery. A pair of two images are captured: one non flashed (image under ambient illumination) and a flashed one. A pure flash image is computed by subtracting the non flashed image from the flashed image. We propose an novel and near real-time algorithm, based on a local illumination model of our flash and the pure flash image, to enhance geometry (from the noisy depth map) and recover reflectance information.

2D shading for cel animation

We present a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and state of the art.

Automatic light compositing using rendered images

Lighting is a key element in photography. Professional photographers often work with complex lighting setups to directly capture an image close to the targeted one. Some photographers reversed this traditional workflow. Indeed, they capture the scene under several lighting conditions, then combine the captured images to get the expected one. Acquiring such a set of images is a tedious task and combining them requires some skill in photography. We propose a fully automatic method, that renders, based on a 3D reconstructed model (shape and albedo), a set of images corresponding to several lighting conditions. The resulting images are combined using a genetic optimization algorithm to match the desired lighting provided by the user as an image.