7,827 research outputs found
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
3D Object Class Detection in the Wild
Object class detection has been a synonym for 2D bounding box localization
for the longest time, fueled by the success of powerful statistical learning
techniques, combined with robust image representations. Only recently, there
has been a growing interest in revisiting the promise of computer vision from
the early days: to precisely delineate the contents of a visual scene, object
by object, in 3D. In this paper, we draw from recent advances in object
detection and 2D-3D object lifting in order to design an object class detector
that is particularly tailored towards 3D object class detection. Our 3D object
class detection method consists of several stages gradually enriching the
object detection output with object viewpoint, keypoints and 3D shape
estimates. Following careful design, in each stage it constantly improves the
performance and achieves state-ofthe-art performance in simultaneous 2D
bounding box and viewpoint estimation on the challenging Pascal3D+ dataset
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