We propose a novel approach to recovering translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with surrounding environment. While existing methods suggested new capture systems or developed the depth distortion models, their solutions were less practical because of strict assumptions or heavy computational complexity. In this paper, we adopt deep residual networks for modeling the ToF depth distortion caused by translucency. To fully utilize both the local and semantic information of objects, multi-scale patches are used to predict the depth value. Based on the quantitative and qualitative evaluation on our benchmark database, we show the effectiveness and robustness of the proposed algorithm.
Shim and Lee @cite_21 also reported the same analysis, and suggested the depth distortion model based on a ToF depth sensing principle for reconstructing translucent objects. They showed that their model is effective to restore the 3D shape of translucent objects assuming a known background and ignoring the effect of light refraction. Later, Kim and Shim @cite_5 extended the idea of @cite_21 and improved the performance by integrating user interactions. Still, these models assume no refraction, limiting the pose of target object being frontal and the shape being planar. Such assumptions are too strict for practical applications.
@cite_5 @cite_21
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Translucency introduces great challenges to 3-D acquisition because of complicated light behaviors such as refraction and transmittance. In this paper, we describe the development of a unified 3-D data acquisition framework that reconstructs translucent objects using a single commercial time-of-flight (ToF) camera. In our capture scenario, we record a depth map and intensity image of the scene twice using a static ToF camera; first, we capture the depth map and intensity image of an arbitrary background, and then we position the translucent foreground object and record a second depth map and intensity image with both the foreground and the background. As a result of material characteristics, the translucent object yields systematic distortions in the depth map. We developed a new distance representation that interprets the depth distortion induced as a result of translucency. By analyzing ToF depth sensing principles, we constructed a distance model governed by the level of translucency, foreground depth, and background depth. Using an analysis-by-synthesis approach, we can recover the 3-D geometry of a translucent object from a pair of depth maps and their intensity images. Extensive evaluation and case studies demonstrate that our method is effective for modeling the nonlinear depth distortion due to translucency and for reconstruction of a 3-D translucent object. This paper presents a method for recovering shape and normal of a transparent object from a single viewpoint using a Time-of-Flight (ToF) camera. Our method is built upon the fact that the speed of light varies with the refractive index of the medium and therefore the depth measurement of a transparent object with a ToF camera may be distorted. We show that, from this ToF distortion, the refractive light path can be uniquely determined by estimating a single parameter. We estimate this parameter by introducing a surface normal consistency between the one determined by a light path candidate and the other computed from the corresponding shape. The proposed method is evaluated by both simulation and real-world experiments and shows faithful transparent shape recovery. We present a new shape-from-distortion framework for recovering specular (reflective refractive) surfaces. While most existing approaches rely on accurate correspondences between 2D pixels and 3D points, we focus on analyzing the curved images of 3D lines which we call curved line images or CLIs. Our approach models CLIs of local reflections or refractions using the recently proposed general linear cameras (GLCs). We first characterize all possible CLIs in a GLC. We show that a 3D line will appear as a conic in any GLC. For a fixed GLC, the conic type is invariant to the position and orientation of the line and is determined by the GLC parameters. Furthermore, CLIs under single reflection refraction can only be lines or hyperbolas. Based on our new theory, we develop efficient algorithms to use multiple CLIs to recover the GLC camera parameters. We then apply the curvature-GLC theory to derive the Gaussian and mean curvatures from the GLC intrinsics. This leads to a complete distortion-based reconstruction framework. Unlike conventional correspondence-based approaches that are sensitive to image distortions, our approach benefits from the CLI distortions. Finally, we demonstrate applying our framework for recovering curvature fields on both synthetic and real specular surfaces. We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque transparent infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.