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Robust Pixel Classification for 3D Modeling with Structured Light

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Robust Pixel Classification for 3D Modeling with Structured Light Robust Pixel Classification for 3D Modeling with Structured Light Yi Xu Daniel G. Aliaga Department of Computer Science Purdue University {xu43|aliaga}@cs.purdue.edu ABSTRACT (a) (b) Modeling 3D objects and scenes is an important part of computer graphics. One approach to modeling is projecting binary patterns onto the scene in order to obtain correspondences and reconstruct ... ... a densely sampled 3D model. In such structured light systems, ... ... determining whether a pixel is directly illuminated by the projector is essential to decoding the patterns. In this paper, we Binary pattern structured Our pixel classification introduce a robust, efficient, and easy to implement pixel light images algorithm classification algorithm for this purpose. Our method correctly establishes the lower and upper bounds of the possible intensity values of an illuminated pixel and of a non-illuminated pixel. Correspondence and reconstruction Based on the two intervals, our method classifies a pixel by determining whether its intensity is within one interval and not in the other. Experiments show that our method improves both the quantity of decoded pixels and the quality of the final (c) (d) reconstruction producing a dense set of 3D points, inclusively for complex scenes with indirect lighting effects. Furthermore, our method does not require newly designed patterns; therefore, it can be easily applied to previously captured data. CR Categories: I.3 [Computer Graphics], I.3.7 [Three- Dimensional Graphics and Realism], I.4 [Image Processing and Computer Vision], I.4.1 [Digitization and Image Capture]. Point cloud Our improved point cloud Keywords: structured light, direct and global separation, 3D Figure 1. System Pipeline. a) We show the binary pattern reconstruction. structured light images. b) Pixels are classified using our algorithm. White/black/gray means on/off/uncertain. c) Points 1 INTRODUCTION are reconstructed using a standard classification method. d) Modeling real-world scenes plays an important role in computer In comparison, our algorithm robustly produces more points: graphics, virtual reality, historical site preservation, and other 201528 points vs. 57464 points. commercial applications. One option is to use structured light systems that make use of lighting devices (e.g., digital projector) samples, previous methods attempt to adaptively compute a pixel to encode scene points by projecting illumination patterns and threshold value, to project pattern images and their inverses, to taking images of the scene under each pattern. Pixels with the use several camera exposure times, to project multiple patterns same codeword are corresponded and triangulated to obtain dense with different intensities, or to use post-processing to clean-up 3D scene samples allowing for point-based modeling, rendering, classification (e.g., [5][6][8]). and other graphics applications [2]. One commonly used Most of these methods assume that a scene point is brighter illumination pattern is the binary pattern, which is an image of when it is illuminated. However, this assumption only holds when interleaving black and white stripes. Each illumination pattern sets a scene point has a weak indirect light component. Consider the one bit of the codeword of a pixel according to whether the following counter-intuitive examples. (1) If a scene point is in corresponding scene point is directly illuminated or not. shadow, it should have zero intensity under any illumination Determining whether a pixel is on or off under an illumination pattern. However, due to inter-reflection from other surface pattern is an important and challenging problem in structured light patches, the point might have large illumination intensity and thus systems. Ideally, applying a threshold to a captured image yields a be classified incorrectly. (2) A scene point may appear dark simple binary image for pixel classification. However in practice, despite being directly illuminated if the part of the scene from the unknown and potentially complex surface and illumination which it would normally receive a significant amount of indirect properties of the scene make such a simple method prone to many light is currently not lit. Yet, projecting a slightly different pattern classification errors. This leads to incorrect correspondences and might illuminate the source of the indirect light and make the thus either to a bad reconstruction or to many samples being lost. same point appear very bright even if it is now not directly To achieve more accurate classification and greater number of illuminated. While the total direct and total indirect illumination components of a fully lit scene can be separated without knowledge of scene geometry (e.g., [3]), the indirect illumination component for arbitrary projected patterns depends both on the pattern and on the scene geometry. This produces the chicken- and-egg problem of needing to know scene geometry before including strong indirect lighting effects. Trobina [8] presented a identifying scene illumination properties and needing to know way to threshold the images by using a single threshold for every scene illumination properties in order to perform robust pixel. The per-pixel threshold is computed by taking images under structured-light scene acquisition. all-white and all-black patterns and averaging the two. The author Our key observation is that for each pixel, we can estimate tight demonstrated that using a pattern and its inverse yields more intensity value bounds for when the pixel is on and for when it is accurate results. The same strategy is also used by Scharstein and off. Knowing these intervals, we can accurately classify a pixel Szeliki [5]. Each pixel is classified based on whether the pixel or when its intensity value falls into one interval but not in the other. its inverse is brighter. These standard methods will not work well Our method bounds the two intervals of a pixel based on the when the scene has strong indirect lighting effects. On the other following facts: a pixel is on only if its intensity includes the hand, our method produces better classification in such scenarios direct component; and, the indirect component of a pixel under a and also recognizes when a pixel cannot be robustly classified. black-and-white stripe pattern is smaller than the total indirect To achieve higher accuracy, previous methods also utilize component of the same pixel. This last assumption is easily true different exposure times [5], multiple intensity illumination since only some of the projector’s pixels are on during any of the images [6] and post-processing to clean up classification results binary patterns. Our algorithm produces a significantly more [5]. In this paper, our focus is on improving the pixel robust and accurate classification. classification and structured-light acquisition early in the capture In this paper, we present a pixel classification algorithm for process. This way, we produce better samples sooner and more structured light systems using binary patterns (Figure 1). First, the robustly. Nevertheless, our algorithm also works with more higher frequency binary patterns are used to separate the direct sophisticated methods such as multiple exposures, multiple and indirect components of each pixel, as well as to encode the intensities images, and optional post-processing steps. pixel codeword. Then, our algorithm computes each pixel’s illuminated interval and the non-illuminated interval. A pixel is 2.2 Direct and Indirect Illumination Components classified according to the two intervals. Pixels that cannot be The intensity of a pixel in a photograph can be decomposed into classified are labeled as uncertain (Figure 1b). The classification the direct component and indirect (or global) component. The results are fed to a reconstruction engine to demonstrate the direct component is due to light bouncing off the surface in a quality of the classification (Figure 1d). We have captured and single reflection. The indirect component is due to multiple applied our algorithm to several real-world scenes. Moreover, reflections (e.g., inter-reflections, subsurface scattering, etc.). since our method only uses the same binary patterns as in a Seitz et al. proposed an inverse light transport theory [7] to standard structured light system, it can be easily applied to estimate the inter-reflection component for Lambertian surfaces. previously captured data. Results show that, as compared to naïve This method requires a large number of images to compute methods, our algorithm has two significant benefits: it increases matrices used in an inter-reflection cancellation process. the total number of decoded pixels and it improves the quality of Nayar et al. presented a fast method to separate the direct and the reconstruction. On average, our algorithm reconstructs 2-7 global components of a scene lit by a single light source using times more points for a variety of objects and surface types. high frequency illumination patterns [3]. In theory, a high Our major contributions are frequency pattern and its inverse are enough to do the separation. • a method to determine the intensity intervals of a pixel In practice, more pattern images, such as a shifting chess board when it is illuminated or non-illuminated during patterns, are used to compensate for the low resolution of the structured-light acquisition, and projector. As pointed out by the authors, the higher frequency • a pixel classification algorithm which allows structured
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