Teaser: Starting from a noisy incomplete raw scan our algorithm iteratively analyzes and extracts a curve network with associated cross-sectional profiles providing a reconstructed model. The extracted high-level shape representation enables easy, intuitive, yet powerful geometry editing. Note that our algorithm is targeted towards delicate 1D features and fails to detect the small disc at the top of the stool.

Man-made objects made out of tubular components, like bent metal, wood, or other decorative materials, are quite common in furniture design, metal sculpture, and wire art (see the following pictures of a few such objects).  However, scanning such complex shapes typically results in large holes, noise, and outliers, and recovering such shapes is rather nontrivial because when the tubular regions meet and overlap, especially nearly tangentially or merge, this can be pretty difficult to interpret.

With the shape prior knowledge that the shapes can be primarily described by 1D structures, we aim to reconstruct and recover the topology of the network of such tubular shapes with complex topology and geometric details from scanned data with noise, outliers, and missing parts. We have shown that using the correct semantic model, called arterial snakes, during reconstruction gives much better results than reconstructing using a general algorithm, and then extracting the semantics of the shape.

Abstract

Man-made objects often consist of detailed and interleaving structures, which are created using cane, coils, metal wires, rods, etc. The delicate structures, although manufactured using simple procedures, are challenging to scan and reconstruct. We observe that such structures are inherently 1D in nature, and hence are naturally represented using an arrangement of generating curves. We refer to the resultant surfaces as arterial surfaces. In this paper we propose a novel approach for analyzing, reconstructing, and manipulating such arterial surfaces. The core of the algorithm is a novel deformable model, called arterial snake, that simultaneously captures the topology and geometry of the arterial objects. The recovered snakes produce a natural decomposition of the raw scans, with the decomposed parts often capturing meaningful object sections. We demonstrate the robustness of our algorithm on a variety of arterial objects corrupted with noise, outliers, and with large parts missing. We present a range of applications including reconstruction, topology repairing, and manipulation of arterial surfaces by directly controlling the underlying curve network and the associated sectional profiles, which are otherwise challenging to perform.
　

Our paper appears in Technical Papers Trailer (*.mp4, 13.9M) (watch on YouTube)
　

We thank the anonymous reviewers for their constructive comments. Several people helped in generating comparison results for Figure 12 namely Junjie Cao and Oscar Au for Laplacian contraction, Misha Kazhdan for Poisson surface reconstruction, Balint Miklos for scale axis computation, Cengiz Oztireli for kernelregression reconstruction, Andrea Tagliasacchi and Richard Hao Zhang for curve skeleton computation, and Guanghua Tan for MPU reconstruction. We thank Min Yue for her help in obtaining the physical models scanned for this paper, Martin Peternell and Johannes Wallner for their help with scanning the models, and Jonathan Balzer for video narration. Ligang Liu is supported by the 973 National Key Basic Research Foundation of China (No. 2009CB320801) and the joint grant of the National Natural Science Foundation of China and Microsoft Research Asia (No. 60776799). Niloy Mitra was partially supported by a Microsoft outstanding young faculty fellowship.
　

Another project page made by Niloy Mitra.

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