Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Birchfield, Stan"'
Autor:
Ye, Yufei, Li, Xueting, Gupta, Abhinav, De Mello, Shalini, Birchfield, Stan, Song, Jiaming, Tulsiani, Shubham, Liu, Sifei
Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for synthesizing an entire image, texture transfer or inserting objects into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::142db27f1c8abde3e0c8d882428d4841
http://arxiv.org/abs/2303.12538
http://arxiv.org/abs/2303.12538
Autor:
Wen, Bowen, Tremblay, Jonathan, Blukis, Valts, Tyree, Stephen, Muller, Thomas, Evans, Alex, Fox, Dieter, Kautz, Jan, Birchfield, Stan
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4026c1996b5d27fdfdc2ef82acc90dd8
Autor:
Blukis, Valts, Lee, Taeyeop, Tremblay, Jonathan, Wen, Bowen, Kweon, In So, Yoon, Kuk-Jin, Fox, Dieter, Birchfield, Stan
We present a unified and compact representation for object rendering, 3D reconstruction, and grasp pose prediction that can be inferred from a single image within a few seconds. We achieve this by leveraging recent advances in the Neural Radiance Fie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86e3df354cd1dd58c8da81625790298c
Autor:
Tang, Zhenggang, Sundaralingam, Balakumar, Tremblay, Jonathan, Wen, Bowen, Yuan, Ye, Tyree, Stephen, Loop, Charles, Schwing, Alexander, Birchfield, Stan
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of an RGB camera (without depth) that is either handheld or mounted on th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1401d8e298d3c60bdd10270659af1f76
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0ff2831eaa9d31928bbc0030c0e1a7d
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as wel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6d034654800d8d9630bb5e354e542bd
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
We present a system for multi-level scene awareness for robotic manipulation. Given a sequence of camera-in-hand RGB images, the system calculates three types of information: 1) a point cloud representation of all the surfaces in the scene, for the p
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint-level controller that dispenses with differential equations entirely.
Autor:
Kamenev, Alexey, Wang, Lirui, Bohan, Ollin Boer, Kulkarni, Ishwar, Kartal, Bilal, Molchanov, Artem, Birchfield, Stan, Nistér, David, Smolyanskiy, Nikolai
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts the motion of all surrounding traffic agents together with the ego-v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f9b00bd7abc685f465f0de80cb6589d
http://arxiv.org/abs/2109.11094
http://arxiv.org/abs/2109.11094
Prior work on 6-DoF object pose estimation has largely focused on instance-level processing, in which a textured CAD model is available for each object being detected. Category-level 6-DoF pose estimation represents an important step toward developin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d419824d4dced6eb9a97b8fcf450b324
http://arxiv.org/abs/2109.06161
http://arxiv.org/abs/2109.06161