Reconstructing delicate geometric details with consumer RGB-D sensors is challenging due to sensor depth and poses uncertainties. To tackle this problem, we propose a unique geometry-guided fusion framework: 1) First, we characterize fusion correspondences with the geodesic curves derived from the mass transport problem, also known as the Monge-Kantorovich problem. Compared with the depth map back-projection methods, the geodesic curves reveal the geometric structures of the local surface. 2) Moving the points along the geodesic curves is the core of our fusion approach, guided by local geometric properties, i.e., Gaussian curvature and mean curvature. Compared with the state-of-the-art methods, our novel geometry-guided displacement interpolation fully utilizes the meaningful geometric features of the local surface. It makes the reconstruction accuracy and completeness improved. Finally, a significant number of experimental results on real object data verify the superior performance of the proposed method. Our technique achieves the most delicate geometric details on thin objects for which the original depth map back-projection fusion scheme suffers from severe artifacts (See Fig. 1).

The ambiguity in image matching is one of main factors decreasing the quality of the 3D model reconstructed by PatchMatch based multiple view stereo. In this paper, we present a novel method, matching ambiguity reduced multiple view stereo (MARMVS) to address this issue. The MARMVS handles the ambiguity in image matching process with three newly proposed strategies: 1) The matching ambiguity is measured by the differential geometry property of image surface with epipolar constraint, which is used as a critical criterion for optimal scale selection of every single pixel with corresponding neighbouring images. 2) The depth of every pixel is initialized to be more close to the true depth by utilizing the depths of its surrounding sparse feature points, which yields faster convergency speed in the following PatchMatch stereo and alleviates the ambiguity introduced by self similar structures of the image. 3) In the last propagation of the PatchMatch stereo, higher priorities are given to those planes with the related 2D image patch possesses less ambiguity, this strategy further propagates a correctly reconstructed surface to raw texture regions. In addition, the proposed method is very efficient even running on consumer grade CPUs, due to proper parameterization and discretization in the depth map computation step. The MARMVS is validated on public benchmarks, and experimental results demonstrate competing performance against the state of the art.

In depth discontinuous and untextured regions, depth maps created by multiple view stereopsis are with heavy noises, but existing depth map fusion methods cannot handle it explicitly. To tackle the problem, two novel strategies are proposed: 1) a more discriminative fusion method, which is based on geometry consistency, measuring the consistency, and stability of surface geometry computed on both partial and global surfaces, different from traditional methods only using visibility consistency; 2) a graph optimization method which fuses pyramids of depth maps as mutual complementary information is available in different scales, and differs from existing multi-scale fusion methods. The method considers both sampling scale of a point and relations among points, and is proven to be solvable by graph cuts. Experimental results verify the superior performance of the proposed method to the traditional visibility consistency-based methods, and the proposed method is also compared favorably with a number of state-of-the-art methods. Moreover, the proposed method achieves the highest completeness among all the methods compared.