Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

Hinterstoißer, Stefan; Lepetit, Vincent; Ilić, Slobodan; Hölzer, Stefan M.; Bradski, Gary; Konolige, Kurt; Navab, Nassir

doi:10.1007/978-3-642-37331-2_42

Cited by 830 publications

(1,312 citation statements)

References 29 publications

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“…Instance recognition in the presence of clutter and occlusion has several important applications, particularly in robotics and augmented reality. Recent methods [9,10,16,6] have begun to exploit the availability of cheap depth sensors to achieve success in instance recognition. Unlike earlier methods that relied on clean laser scanned data [14], these devices have encouraged research into the use of cheap depth data for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

“…In the Desk3D dataset, each scene point-cloud is obtained by integrating few frames of depth data from a Kinect sensor to aid in better extraction of edge features. Moreover, to better demonstrate the benefits of our learning based recognition scheme, we also use the publicly available dataset (ACCV3D) [10] which has the largest number of test pose variations in the presence of heavy clutter with no occlusion. We benchmark our algorithm by extensive evaluation on Desk3D as well as on ACCV3D and show that our learning based method clearly outperforms the state-of-the-art recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Robust Instance Recognition in Presence of Occlusion and Clutter

Bonde

Badrinarayanan

Cipolla

2014

Computer Vision – ECCV 2014

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Abstract. We present a robust learning based instance recognition framework from single view point clouds. Our framework is able to handle real-world instance recognition challenges, i.e, clutter, similar looking distractors and occlusion. Recent algorithms have separately tried to address the problem of clutter [9] and occlusion [16] but fail when these challenges are combined. In comparison we handle all challenges within a single framework. Our framework uses a soft label Random Forest [5] to learn discriminative shape features of an object and use them to classify both its location and pose. We propose a novel iterative training scheme for forests which maximizes the margin between classes to improve recognition accuracy, as compared to a conventional training procedure. The learnt forest outperforms template matching, DPM [7] in presence of similar looking distractors. Using occlusion information, computed from the depth data, the forest learns to emphasize the shape features from the visible regions thus making it robust to occlusion. We benchmark our system with the state-of-the-art recognition systems [9,7] in challenging scenes drawn from the largest publicly available dataset. To complement the lack of occlusion tests in this dataset, we introduce our Desk3D dataset and demonstrate that our algorithm outperforms other methods in all settings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Instance Recognition in Presence of Occlusion and Clutter

Bonde

Badrinarayanan

Cipolla

2014

Computer Vision – ECCV 2014

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“…TUM Texture-Less 3D Objects dataset [36] was constructed by Technical University of Munich in 2012. It can be widely used for object segmentation, automatic modeling and 3D object tracking.…”

Section: Tum Texture-less 3d Objects Datasetmentioning

confidence: 99%

“…With respect to the ground truth, 6DOF pose was labeled for each object in each image. More details about this dataset can be found in [36,37].…”

Section: Tum Texture-less 3d Objects Datasetmentioning

confidence: 99%

RGB-D datasets using microsoft kinect or similar sensors: a survey

Cai

Han

Liu

et al. 2016

Multimed Tools Appl

123

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RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the stateof-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms.

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“…The approaches to textureless object detection and/or pose estimation divide into two broad categories, model-based and image-based. Modelbased approaches have used CAD 3-D models [9,10] which is common in industrial applications, or depth information [8,11].…”

Section: Introductionmentioning

confidence: 99%

Fast Detection of Multiple Textureless 3-D Objects

Cai

Werner

Matas

2013

Lecture Notes in Computer Science

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Abstract. We propose a fast edge-based approach for detection and approximate pose estimation of multiple textureless objects in a single image. The objects are trained from a set of edge maps, each showing one object in one pose. To each scanning window in the input image, the nearest neighbor is found among these training templates by a twolevel cascade. The first cascade level, based on a novel edge-based sparse image descriptor and fast search by index table, prunes the majority of background windows. The second level verifies the surviving detection hypotheses by oriented chamfer matching, improved by selecting discriminative edges and by compensating a bias towards simple objects. The method outperforms the state-of-the-art approach by Damen et al. (2012). The processing is near real-time, ranging from 2 to 4 frames per second for the training set size ∼10 4 .

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Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

Cited by 830 publications

References 29 publications

Robust Instance Recognition in Presence of Occlusion and Clutter

Robust Instance Recognition in Presence of Occlusion and Clutter

RGB-D datasets using microsoft kinect or similar sensors: a survey

Fast Detection of Multiple Textureless 3-D Objects

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