Shape from Video: Dense Shape, Texture, Motion and Lighting from Monocular Image Streams
نویسندگان
چکیده
This paper presents a probabilistic framework for robust recovery of dense 3D shape, motion, texture and lighting from monocular image streams. We assume that the object is smooth, Lambertian, illuminated by one distant light source, and subject to smoothly-varying rigid motion. The problem is formulated as a MAP estimation problem in which all shape, motion, noise variances and outlier probabilities are estimated simultaneously. Estimation is performed using a multi-stage initialization process followed by a large-scale quasi-Newtonian optimization technique.
منابع مشابه
New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor
Background: One of the challenges of PET/MRI combined systems is to derive an attenuation map to correct the PET image. For that, the pseudo-CT image could be used to correct the attenuation. Until now, most existing scientific researches construct this pseudo-CT image using the registration techniques. However, these techniques suffer from the local minima of the non-rigid deformation energy f...
متن کاملMulti-View 3D Shape and Motion Recovery on the Spatio-Temporal Curve Manifold
In this paper we consider the problem of recovering the 3D motion and shape of an arbitrarily-moving, arbitrarilyshaped curve from multiple synchronized video streams acquired from distinct and known points in space. By studying the 3D motion and shape constraints provided by the input video streams, we show that (1) shape and motion recovery is equivalent to the problem of recovering the diffe...
متن کاملHigh quality dynamic reflectance and surface reconstruction from video
The creation of high quality animations of real-world human actors has long been a challenging problem in computer graphics. It involves the modeling of the shape of the virtual actors, creating their motion, and the reproduction of very fine dynamic details. In order to render the actor under arbitrary lighting, it is required that reflectance properties are modeled for each point on the surfa...
متن کاملOptical Flow-Based 3D Human Motion Estimation from Monocular Video
We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing the difference between computed flow fields and the output of an artificial flo...
متن کاملGeneration, Visualization, and Editing of 3D Video
3D video is the ultimate image medium recording dynamic visual events in the real world as is. Recorded object behaviors can be observed from any viewpoint, because 3D video records the object’s full 3D shape, motion, and precise surface properties (i.e. color and texture). In our last paper[1], we presented a method of reconstructing dynamic 3D object shape from multi-view video images, by whi...
متن کامل