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A volumetric method for building complex models from range images

A volumetric method for building complex models from range images,10.1145/237170.237269,Brian Curless,Marc Levoy

A volumetric method for building complex models from range images   (Citations: 937)
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A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating,representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties.Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time,we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously.We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model,we tessellate over the boundaries between regions seen to be empty and regions never observed.Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles.
Conference: Annual Conference on Computer Graphics - SIGGRAPH , pp. 303-312, 1996
Cumulative Annual
    • ...Curless and Levoy (1996) proposed a volumetric approach which exploited the fact that the point clouds were a collection of laser range images...

    Z. M. Biet al. Sensing and responding to the changes of geometric surfaces in flexibl...

    • ...Most other algorithms reconstruct an approximating surface represented in implicit forms, including signed distance functions [3], [9], [10], radial basis functions [11], [12], [13], moving least square surfaces [14], [15], [16], and indicator functions [2]...

    Kun Zhouet al. Data-Parallel Octrees for Surface Reconstruction

    • ...Another typical approach is to capture images of the object in a controlled environment like a multi-camera studio with mono-color screen (Franco and Boyer 2003; Starck and Hilton 2007 ;V lasic et al.2008; Curless and Levoy 1996; Chen and Medioni 1992; Fitzgibbon et al. 1998) or structured lighting (Zhang et al. 2002), and then use something like a shape-from-silhouette algorithm (Szeliski 1993; Fang et al. 2003; Chen et al. 2008; Forbes ...

    Dhruv Batraet al. Interactively Co-segmentating Topically Related Images with Intelligen...

    • ...Many improvements are possible: a better use of visibility in view point selection, applying merging methods like (Curless and Levoy 1996; Soucy and Laurendeau 1995) after view point selection...

    Maxime Lhuillier. A Generic Error Model and Its Application to Automatic 3D Modeling of ...

    • ...A related non-parametric representation used in graphics is the signed distance function (SDF) introduced in [7] for the purpose of fusing partial depth scans while mitigating problems related to mesh-based reconstruction algorithms.,In practice the result is only locally true due to surface occlusions and truncation of the SDF as detailed in [7] is required to avoid surfaces interfering.,This was noted in [7] and has an important consequence in our work where we utilise the full currently reconstructed surface model to obtain a prediction for use in live sensor pose estimation.,The final models were optimised off-line using [7].,Each consecutive depth frame, with an associated live camera pose estimate, is fused incrementally into one single 3D reconstruction using the volumetric truncated signed distance function (TSDF) [7].,Storing a weight Wk(p) with each value allows an important aspect of the global minimum of the convexL2 de-noising metric to be exploited for real-time fusion; that the solution can be obtained incrementally as more data terms are added using a simple weighted running average [7], defined point-wisefpjFRk(p)6 nullg:...

    Richard A. Newcombeet al. KinectFusion: Real-time dense surface mapping and tracking

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