A Comparison of PMD-Cameras and Stereo-Vision for the Task of Surface Reconstruction using Patchlets

Beder, Christian; Bartczak, Bogumil; Koch, Reinhard

doi:10.1109/cvpr.2007.383348

Cited by 62 publications

(46 citation statements)

References 16 publications

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“…Their analysis shows that range data adds robustness to the model, simplifies some preprocessing steps, and in general the generated models capture better the nature of the object. Stereo and ToF have also been compared by Beder et al [40] in the framework of surface patchlet identification and pose estimation. In their setup, using a highly textured surface for stereo experiments, ToF slightly outperforms stereo in terms of depth and normal direction to the patchlet.…”

Section: Object-related Tasksmentioning

confidence: 99%

“…A classical solution in the area of object modeling is the use of calibrated stereo rigs. Therefore, initial works were devoted to their comparison with [37] Dynamic object detection and classification Color and light independence PMD Hussmann and Liepert [38] Object pose Easy object/background segmentation PMD Guomundsson et al [39] Known object pose estimation Light independent / Absolute scale SR3 Beder et al [40] Surface reconstruction using patchlets ToF easily combines with stereo PMD Fuchs and May [7] Precise surface reconstruction 3D at high rate SR3/O3D100 (Depth) Dellen et al [5] 3D object reconstruction 3D at high rate SR3 (Depth) Foix et al [6] Kuehnle et al [8] Object recognition for grasping 3D allow geometric primitives search SR3 Grundmann et al [41] Collision free object manipulation 3D at high rate SR3 + stereo Reiser and Kubacki [42] Position based visual servoing 3D is simply obtained / No model needed SR3 (Depth) Gachter et al [43] Object part detection for classification 3D at high rate SR3 Shin et al [44] SR2 Klank et al [45] Mobile manipulation Easy table/object segmentation SR4 Marton et al [46] Object categorization ToF easily combines with stereo SR4 + color Nakamura et al [47] Mobile manipulation Easy table segmentation SR4 + color Saxena et al [9] Grasping unknown objects 3D at high rate SR3 + stereo Zhu et al [48] Short range depth maps ToF easily combines with stereo SR3 + stereo Lindner et al [49] Object segmentation for recognition Easy color registration PMD + color camera Fischer et al [50] Occlusion handling in virtual objects 3D at high rate PMD + color camera…”

Section: Object-related Tasksmentioning

confidence: 99%

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ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited.After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.

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Section: Object-related Tasksmentioning

confidence: 99%

Section: Object-related Tasksmentioning

confidence: 99%

ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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“…Sensor fusion. The different reconstruction methods reviewed here provide different, often complementary advantages, as already shown by Groch et al (Groch et al, 2011) for laparoscopic interventions as well as by Beder et al (Beder et al, 2007) and Ringbeck (Ringbeck, 2009) in the non-medical context. For example, stereo approaches perform best on textured objects, while structured light and ToF yield the best results on homogeneous objects.…”

Section: Discussionmentioning

confidence: 85%

“…Due to the above-mentioned systematic distance errors, ToF camera calibration not only requires a standard lateral calibration to determine the intrinsic camera parameters (Beder et al, 2007;Lindner et al, 2010), but also an additional calibration procedure to compensate for depth errors. Further challenges to be addressed include so-called flying pixels (i.e.…”

Section: State-of-the-artmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

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One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilites by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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“…On the other hand, TOF camera estimates the distance from the real object to the eye with the help of time-of-flight principle, that measures the time a signal travels, with well defined speed spends, from the transmitter to the receiver (Beder, et al, 2007). The chosen PMD CamCube 3.0 utilizes Radio Frequency (RF) modulated light sources with phase detectors.…”

Section: Fig1: Two Stages Renderingmentioning

confidence: 99%

Occlusion Handling Method for Ubiquitous Augmented Reality Using Reality Capture Technology and GLSL

Dong¹,

Feng²,

Kamat³

2011

Computing in Civil Engineering (2011)

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ABSTRACT:The primary challenge in generating convincing Augmented Reality (AR) graphics is to project 3D models onto a user's view of the real world and create a temporal and spatial sustained illusion that the virtual and real objects co-exist. Regardless of the spatial relationship between the real and virtual objects, traditional AR graphical engines break the illusion of co-existence by displaying the real world merely as a background, and superimposing virtual objects on the foreground. This research proposes a robust depth sensing and frame buffer algorithm for handling occlusion problems in ubiquitous AR applications. A high-accuracy Time-of-flight (TOF) camera is used to capture the depth map of the real-world in real time. The distance information is processed using the OpenGL Shading Language (GLSL) and rendered into the graphics depth buffer, allowing accurate depth resolution and hidden surface removal in composite AR scenes. The designed algorithm is validated in several indoor and outdoor experiments using the SMART AR framework.

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A Comparison of PMD-Cameras and Stereo-Vision for the Task of Surface Reconstruction using Patchlets

Cited by 62 publications

References 16 publications

ToF cameras for active vision in robotics

ToF cameras for active vision in robotics

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Occlusion Handling Method for Ubiquitous Augmented Reality Using Reality Capture Technology and GLSL

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