A new image-based rendering method, based on the light field and Lumigraph system, allows illumination to be changed interactively. It does not try to recover or use any geometrical information (e.g., depth or surface normals) to calculate the illumination, but the resulting images are physically correct. The scene is first sampled from different viewpoints and under different illuminations. Treating each pixel on the back plane of the light slab as a surface element, the sampled images are used to find an apparent BRDF of each surface element. The tabular BRDF data of each pixel is further transformed to the spherical harmonic domain for efficient storage. Whenever the user changes the illumination setting, a certain number of views are reconstructed. The correct user perspective view is then displayed using the texture mapping technique of the Lumigraph system. Hence, the intensity, the type and the number of the light sources can be manipulated interactively.

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A n o vel model-based pose estimation algorithm is presented which estimates the motion of a three-dimensional object from an image sequence. The nonlinear estimation process within iteration is divided into two linear estimation stages, namely the depth approximation and the pose calculation. In the depth approximation stage, the depths of the feature points in three-dimensional space are estimated. In the pose calculation stage, the rotation and translation parameters between the estimated feature points and the model point set are calculated by a fast singular value decomposition method. The whole process is executed recursively until the result is stable. Since both stages can be solved e ciently, the computational cost is low. As a result, the algorithm is well-suited for real time computer vision applications. We demonstrate the capability o f t h i s algorithm by applying it to a real time head tracking problem. The results are satisfactory.

A new data representation of image‐based objects is presented. With this representation, the user can change the illumination as well as the viewpoint of an image‐based scene. Physically correct imagery can be generated without knowing any geometrical information (e.g. depth or surface normal) of the scene. By treating each pixel on the image plane as a surface element, we can measure its apparent BRDF (bidirectional reflectance distribution function) by collecting information in the sampled images. These BRDFs allow us to calculate the correct pixel colour under a new illumination set‐up by fitting the intensity, direction and number of the light sources. We demonstrate that the proposed representation allows re‐rendering of the scene illuminated by different types of light sources. Moreover, two compression schemes, spherical harmonics and discrete cosine transform, are proposed to compress the huge amount of tabular BRDF data. © 1998 John Wiley & Sons, Ltd.

A n o vel model-based pose estimation algorithm is presented which estimates the motion of a three-dimensional object from an image sequence. The nonlinear estimation process within iteration is divided into two linear estimation stages, namely the depth approximation and the pose calculation. In the depth approximation stage, the depths of the feature points in three-dimensional space are estimated. In the pose calculation stage, the rotation and translation parameters between the estimated feature points and the model point set are calculated by a fast singular value decomposition method. The whole process is executed recursively until the result is stable. Since both stages can be solved e ciently, the computational cost is low. As a result, the algorithm is well-suited for real time computer vision applications. We demonstrate the capability o f t h i s algorithm by applying it to a real time head tracking problem. The results are satisfactory.