Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Labbe, Yann"'
Autor:
Hodan, Tomas, Sundermeyer, Martin, Labbe, Yann, Nguyen, Van Nguyen, Wang, Gu, Brachmann, Eric, Drost, Bertram, Lepetit, Vincent, Rother, Carsten, Matas, Jiri
We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related
Externí odkaz:
http://arxiv.org/abs/2403.09799
Autor:
Örnek, Evin Pınar, Labbé, Yann, Tekin, Bugra, Ma, Lingni, Keskin, Cem, Forster, Christian, Hodan, Tomas
We propose FoundPose, a model-based method for 6D pose estimation of unseen objects from a single RGB image. The method can quickly onboard new objects using their 3D models without requiring any object- or task-specific training. In contrast, existi
Externí odkaz:
http://arxiv.org/abs/2311.18809
Autor:
Cífka, Martin, Ponimatkin, Georgy, Labbé, Yann, Russell, Bryan, Aubry, Mathieu, Petrik, Vladimir, Sivic, Josef
We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we der
Externí odkaz:
http://arxiv.org/abs/2312.02985
Autor:
Sundermeyer, Martin, Hodan, Tomas, Labbe, Yann, Wang, Gu, Brachmann, Eric, Drost, Bertram, Rother, Carsten, Matas, Jiri
We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image.
Externí odkaz:
http://arxiv.org/abs/2302.13075
Autor:
Labbé, Yann, Manuelli, Lucas, Mousavian, Arsalan, Tyree, Stephen, Birchfield, Stan, Tremblay, Jonathan, Carpentier, Justin, Aubry, Mathieu, Fox, Dieter, Sivic, Josef
We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a region of interest displaying the object in the image and (ii) a CAD mode
Externí odkaz:
http://arxiv.org/abs/2212.06870
We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are twofold. First, we derive
Externí odkaz:
http://arxiv.org/abs/2204.05145
We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact w
Externí odkaz:
http://arxiv.org/abs/2104.09359
Autor:
Hodan, Tomas, Sundermeyer, Martin, Drost, Bertram, Labbe, Yann, Brachmann, Eric, Michel, Frank, Rother, Carsten, Matas, Jiri
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D im
Externí odkaz:
http://arxiv.org/abs/2009.07378
We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to genera
Externí odkaz:
http://arxiv.org/abs/2008.08465
Autor:
Labbé, Yann, Zagoruyko, Sergey, Kalevatykh, Igor, Laptev, Ivan, Carpentier, Justin, Aubry, Mathieu, Sivic, Josef
We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB c
Externí odkaz:
http://arxiv.org/abs/1904.10348