Detecting Fine-Grained Affordances with an Anthropomorphic Agent Model

Seib, Viktor; Wojke, Nicolai; Knauf, Malte; Paulus, Dietrich

doi:10.1007/978-3-319-16181-5_30

Cited by 5 publications

(3 citation statements)

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“…The main idea of this paper is to introduce the concept of fine-grained affordances and over-come the limitations of our previous approach (Seib et al, 2015) that relied on plane segmentation and, thus, was not general enough. Here we have shown that our algorithm is able to differentiate affordances on a fine-grained scale without prior object or plane segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…When fitting the agent into the environment during affordance detection, the edges of the graph are approximated by cylinders for collision detection. Contrary to our previous work (Seib et al, 2015), we do not need an explicit environment model E. Rather, E is simply the point cloud data of a scene where affordances should be detected.…”

Section: Agent and Environmentmentioning

confidence: 99%

“…We propose looking closely at the individual affordances and distinguishing their functional differences an a fine-grained scale. We already introduced the concept of fine-grained affordances in (Seib et al, 2015) to closely resemble the functional differences of related objects. Although good results could be obtained, our previous work was a proof-of-concept with several limitations.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Fine-grained Sitting Affordances with Fuzzy Sets

Seib

Knauf

Paulus

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Recently, object affordances have moved into the focus of researchers in computer vision. Affordances describe how an object can be used by a specific agent. This additional information on the purpose of an object is used to augment the classification process. With the herein proposed approach we aim at bringing affordances and object classification closer together by proposing fine-grained affordances. We present an algorithm that detects fine-grained sitting affordances in point clouds by iteratively transforming a human model into the scene. This approach enables us to distinguish object functionality on a finer-grained scale, thus more closely resembling the different purposes of similar objects. For instance, traditional methods suggest that a stool, chair and armchair all afford sitting. This is also true for our approach, but additionally we distinguish sitting without backrest, with backrest and with armrests. This fine-grained affordance definition closely resembles individual types of sitting and better reflects the purposes of different chairs. We experimentally evaluate our approach and provide fine-grained affordance annotations in a dataset from our lab.

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Section: Discussionmentioning

confidence: 99%

Section: Agent and Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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