Integration of multiple range images is important to make use of 3D data acquired from stereo systems, laser range finders, etc. We propose a new range image integration method based on volumetric representation. Unlike other volume-based integration methods, we adaptively subdivide voxels depending on the curvature of the surface to be reconstructed, providing efficient representation of the underlying geometry and efficient use of computational resources. In our range image merging framework, additional attributes, e.g., color, laser reflectance power, etc., can be taken into account as well as 3D geometric information. This ability allows us to generate 3D models preserving sharp edges around texture boundaries, thereby providing a good basis for efficient rendering and texture mapping. The overall framework is designed to be robust against noise, taking consensus carefully in both geometry and color, which could be suitable for 3D model reconstruction from noisy stereo images. In this paper, we describe the system, and present several results of applying our framework to real data. We also present some other future applications based on our framework.