Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Guizilini, Vitor"'
Autor:
Tian, Stephen, Wulfe, Blake, Sargent, Kyle, Liu, Katherine, Zakharov, Sergey, Guizilini, Vitor, Wu, Jiajun
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, environments, and observational modalities remain elusive. In this work, w
Externí odkaz:
http://arxiv.org/abs/2409.03685
Autor:
Chaudhury, Arkadeep Narayan, Vasiljevic, Igor, Zakharov, Sergey, Guizilini, Vitor, Ambrus, Rares, Narasimhan, Srinivasa, Atkeson, Christopher G.
Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulation, autonomous driving, convenient product capture, and consumer-level
Externí odkaz:
http://arxiv.org/abs/2409.03061
Monocular visual odometry is a key technology in a wide variety of autonomous systems. Relative to traditional feature-based methods, that suffer from failures due to poor lighting, insufficient texture, large motions, etc., recent learning-based SLA
Externí odkaz:
http://arxiv.org/abs/2406.00929
Autor:
Lukoianov, Artem, Borde, Haitz Sáez de Ocáriz, Greenewald, Kristjan, Guizilini, Vitor Campagnolo, Bagautdinov, Timur, Sitzmann, Vincent, Solomon, Justin
While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we s
Externí odkaz:
http://arxiv.org/abs/2405.15891
Autor:
Fang, Jiading, Tan, Xiangshan, Lin, Shengjie, Vasiljevic, Igor, Guizilini, Vitor, Mei, Hongyuan, Ambrus, Rares, Shakhnarovich, Gregory, Walter, Matthew R
If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expressions is challenging -- it requires the ability to both parse the 3D s
Externí odkaz:
http://arxiv.org/abs/2404.19221
Autor:
Irshad, Muhammad Zubair, Zakharov, Sergey, Guizilini, Vitor, Gaidon, Adrien, Kira, Zsolt, Ambrus, Rares
Neural fields excel in computer vision and robotics due to their ability to understand the 3D visual world such as inferring semantics, geometry, and dynamics. Given the capabilities of neural fields in densely representing a 3D scene from 2D images,
Externí odkaz:
http://arxiv.org/abs/2404.01300
Diffusion models (DMs) excel in photo-realistic image synthesis, but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D
Externí odkaz:
http://arxiv.org/abs/2404.00815
Autor:
Iwase, Shun, Liu, Katherine, Guizilini, Vitor, Gaidon, Adrien, Kitani, Kris, Ambrus, Rares, Zakharov, Sergey
We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Despite notable advancements in single-object 3D shape completion, high-quality reconstructions in hig
Externí odkaz:
http://arxiv.org/abs/2403.14628
Autor:
Irshad, Muhammad Zubair, Zakharov, Sergey, Liu, Katherine, Guizilini, Vitor, Kollar, Thomas, Gaidon, Adrien, Kira, Zsolt, Ambrus, Rares
Recent implicit neural representations have shown great results for novel view synthesis. However, existing methods require expensive per-scene optimization from many views hence limiting their application to real-world unbounded urban settings where
Externí odkaz:
http://arxiv.org/abs/2308.12967
Publikováno v:
The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment,
Externí odkaz:
http://arxiv.org/abs/2308.02153