Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Ainetter, Stefan"'
We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects from images using interpretable shape programs. In comparison to traditional CAD model retrieval methods, the use of shape programs for 3D reconstruction allows for rea
Externí odkaz:
http://arxiv.org/abs/2404.10620
We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera. We first investigate various objective functions to measure similarity between a candidate
Externí odkaz:
http://arxiv.org/abs/2309.06107
We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate
Externí odkaz:
http://arxiv.org/abs/2212.11796
In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coordinate Convolution (CoordConv),
Externí odkaz:
http://arxiv.org/abs/2111.11114
In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module
Externí odkaz:
http://arxiv.org/abs/2107.05287
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ff4af7bbc062fb29091ebc9a541bd95
https://doi.org/10.1109/wacv56688.2023.00317
https://doi.org/10.1109/wacv56688.2023.00317
We propose a method to automatically generate high quality ground truth annotations for grasping point prediction and show the usefulness of these annotations by training a deep neural network to predict grasping candidates for objects in a cluttered
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d61b9aecb8cd5b82847290707bfbd43
Autor:
Weiss, Stephan, Ainetter, Stefan, Arneitz, Fred, Arronde, Dailys, Dhakate, Rohit, Fraundorfer, Friedrich, Gietler, Harald, Gubensäk, Wolfgang, Medeiros, Mylena, Stetco, Christian, Zangl, Hubert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6480bbf72308e01b0c560391c442f86c
Autor:
Ainetter, Stefan, Pinz, Axel
Proceedings of the 24th Computer Vision Winter Workshop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74461e5cb03132accb4b7fd2c755f347