Fast Detection of Multiple Textureless 3-D Objects

Cai, Hongping; Werner, Tomáš; Matas, Jiřı́

doi:10.1007/978-3-642-39402-7_11

Cited by 35 publications

(14 citation statements)

References 19 publications

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“…Successively, [27] optimized the matching via a cascaded classification scheme, achieving a run-time increase by a factor of 10. Improvements in efficiency are also achieved by the two-stage cascaded detection method in [7] and by the hashing matching approach tailored to LineMOD templates proposed in [18]. Other recent approaches [21,10,3] build discriminative models based on such representations using SVM or boosting applied to training data.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Kehl

Milletarì

Tombari

et al. 2016

Lecture Notes in Computer Science

265

225

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We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects.

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Section: Related Workmentioning

confidence: 99%

Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Kehl

Milletarì

Tombari

et al. 2016

Lecture Notes in Computer Science

265

225

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“…Visual pe rce pti on i s structured around two major components. The first of these components is concerned with the detection of objects (Cai et al 2013) of interest in RGB-D images that have been captured with the Kinect sensor. Following the detection process, the location in space of detected obje cts i s e sti mated using a 3D pose estimation method from Lourakis & Zabulis (2013).…”

Section: The Memory Representations Of Both the Peripersonal Space Anmentioning

confidence: 99%

Towards a Brain-like Memory for Cognitive Robots. PhD Dissertation, University of Genova, Genova, Italy

Bhat¹

2018

Preprint

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The most striking feature of any cognitive system is its ability to keep learning cumulatively and exploit its experiences prospectively to anticipate, reason and produce goal-oriented behaviors. At the core of this intriguing capability lies our memory. Every touch, sound, sight, taste triggers our memory, enabling us to remember and flexibly connect our past experiences, with the available present to the possible future. While emerging trends in neurosciences are rapidly enriching our understanding of the functional organization of memory in the brain, a prospective memory architecture is also a central design feature necessary if robots are to truly become commonplace assistants in numerous application domains (domestic, industrial, others) and in the complexity of the environments we inhabit and create (where neither everything can be known nor can everything be experienced). Hence, with the central premise that cognition is constructive manipulation of memory, this thesis proposes a novel brain guided perspective on the design of cognitive architectures for cumulatively developing systems. Set up in the context of several experiments from animal and infant cognition reenacted on the iCub humanoid, a principled framework for cumulative learning of actions, skills, affordances and cause-effect relations through multiple streams (imitation, exploration, linguistic inputs) is proposed. How such diverse experiences of the robot learnt in past are recalled based on present context, combined with explorative actions to learn something new, creatively connected to generate novel behaviors in the context of sought goals is demonstrated through several tasks. The proposed integrated machinery provides for a shared computational basis for recalling of the past and simulation of the future in line with the emerging trends from neuroscience (like the brain’s default mode network). A notable transition further is the porting of the framework developed on iCub humanoid to real world industrial settings in tasks like assembly and manufacturing (facilitating runtime reasoning and quick switchover to novel assembly tasks). In this sense, the proposed architecture brings together the fields of Robotics, Developmental Psychology, and Neuroscience to craft cognitive and adaptable End User Applications while at the same time provides insights into the functional organization of the brain. In parallel, it lays the foundation for a new generation of cognitive architectures that are domain agnostic (i.e. support open-ended learning), partially embodiment agnostic (i.e. are configurable to new robotic platforms), partially self-driven (i.e. can generate their own internal goals) and brain guided.

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“…LineMOD [8] performs template matching by extracting contour and surface normal gradient direction information, and achieves efficient matching by unique data lookup strategy. Kehl et al [9] and Cai et al [10] respectively use hashing matching approach and a two-stage cascaded detection method to improve the efficiency. Successively, Rios et al [11] optimized the matching via a cascaded classification scheme.…”

Section: Related Workmentioning

confidence: 99%

Deep learning of directional truncated signed distance function for robust 3D object recognition

Liu

Cong

Wang

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression tasks. To achieve this, we first adopt the Truncated Signed Distance Function (TSDF) to encode the point cloud and extract low compact discriminative feature via unsupervised deep learning network. This approach can not only eliminate the dense scale sampling for offline model training but also reduce the distortion by mapping the 3D shape to the 2D plane and overcome the dependence on color cues. Then, we train a Hough forests to achieve multi-object detection and 6-DoF pose estimation simultaneously. In addition, we propose a robust multilevel verification strategy that effectively reduces the unpredictability of output values which occurs in the hough regression module. Experiments on public datasets demonstrate that our approach provides effective results comparable to the state-of-the-arts.

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Fast Detection of Multiple Textureless 3-D Objects

Cited by 35 publications

References 19 publications

Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Towards a Brain-like Memory for Cognitive Robots. PhD Dissertation, University of Genova, Genova, Italy

Deep learning of directional truncated signed distance function for robust 3D object recognition

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