22,460 research outputs found
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
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