Zobrazeno 1 - 10
of 708
pro vyhledávání: '"Mattoccia, A"'
High frame rate and accurate depth estimation plays an important role in several tasks crucial to robotics and automotive perception. To date, this can be achieved through ToF and LiDAR devices for indoor and outdoor applications, respectively. Howev
Externí odkaz:
http://arxiv.org/abs/2409.08277
Event stereo matching is an emerging technique to estimate depth from neuromorphic cameras; however, events are unlikely to trigger in the absence of motion or the presence of large, untextured regions, making the correspondence problem extremely cha
Externí odkaz:
http://arxiv.org/abs/2408.04633
This paper presents a novel general-purpose stereo and depth data fusion paradigm that mimics the active stereo principle by replacing the unreliable physical pattern projector with a depth sensor. It works by projecting virtual patterns consistent w
Externí odkaz:
http://arxiv.org/abs/2406.04345
Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applications, including face anti-spoofing, saliency detection, and trajecto
Externí odkaz:
http://arxiv.org/abs/2405.10357
Autor:
Tosi, Fabio, Zhang, Youmin, Gong, Ziren, Sandström, Erik, Mattoccia, Stefano, Oswald, Martin R., Poggi, Matteo
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments. This evolution ranges f
Externí odkaz:
http://arxiv.org/abs/2402.13255
Methods for 3D reconstruction from posed frames require prior knowledge about the scene metric range, usually to recover matching cues along the epipolar lines and narrow the search range. However, such prior might not be directly available or estima
Externí odkaz:
http://arxiv.org/abs/2401.14401
This paper proposes a new framework for depth completion robust against domain-shifting issues. It exploits the generalization capability of modern stereo networks to face depth completion, by processing fictitious stereo pairs obtained through a vir
Externí odkaz:
http://arxiv.org/abs/2312.09254
Autor:
Zhao, Chaoqiang, Poggi, Matteo, Tosi, Fabio, Zhou, Lei, Sun, Qiyu, Tang, Yang, Mattoccia, Stefano
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning process by obtaining coarse camera poses from monocular sequences through m
Externí odkaz:
http://arxiv.org/abs/2309.16019
This paper proposes a novel framework integrating the principles of active stereo in standard passive camera systems without a physical pattern projector. We virtually project a pattern over the left and right images according to the sparse measureme
Externí odkaz:
http://arxiv.org/abs/2309.12315
Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we pres
Externí odkaz:
http://arxiv.org/abs/2309.02436