Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Klinghoffer, Tzofi"'
Autor:
Behari, Nikhil, Young, Aaron, Somasundaram, Siddharth, Klinghoffer, Tzofi, Dave, Akshat, Raskar, Ramesh
3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile device
Externí odkaz:
http://arxiv.org/abs/2411.19474
Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view
Externí odkaz:
http://arxiv.org/abs/2411.06181
Autor:
Klinghoffer, Tzofi
Animal's visual perception systems have evolved to their environment over billions of years, enabling them to navigate, avoid predators, and hunt prey. In contrast, machine perception systems designed by humans require significant engineering and oft
Externí odkaz:
https://hdl.handle.net/1721.1/151981
Autor:
Klinghoffer, Tzofi, Xiang, Xiaoyu, Somasundaram, Siddharth, Fan, Yuchen, Richardt, Christian, Raskar, Ramesh, Ranjan, Rakesh
3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF), while popular for view synthesis and 3D reconstruction, are typically reli
Externí odkaz:
http://arxiv.org/abs/2312.14239
Imaging systems consist of cameras to encode visual information about the world and perception models to interpret this encoding. Cameras contain (1) illumination sources, (2) optical elements, and (3) sensors, while perception models use (4) algorit
Externí odkaz:
http://arxiv.org/abs/2309.13851
Autor:
Klinghoffer, Tzofi, Philion, Jonah, Chen, Wenzheng, Litany, Or, Gojcic, Zan, Joo, Jungseock, Raskar, Ramesh, Fidler, Sanja, Alvarez, Jose M.
Autonomous vehicles (AV) require that neural networks used for perception be robust to different viewpoints if they are to be deployed across many types of vehicles without the repeated cost of data collection and labeling for each. AV companies typi
Externí odkaz:
http://arxiv.org/abs/2309.05192
Autor:
Tiwary, Kushagra, Dave, Akshat, Behari, Nikhil, Klinghoffer, Tzofi, Veeraraghavan, Ashok, Raskar, Ramesh
Reflections on glossy objects contain valuable and hidden information about the surrounding environment. By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemi
Externí odkaz:
http://arxiv.org/abs/2212.04531
Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this pap
Externí odkaz:
http://arxiv.org/abs/2204.09871
State-of-the-art methods in generative representation learning yield semantic disentanglement, but typically do not consider physical scene parameters, such as geometry, albedo, lighting, or camera. We posit that inverse rendering, a way to reverse t
Externí odkaz:
http://arxiv.org/abs/2204.05281
We present a method that learns neural shadow fields which are neural scene representations that are only learnt from the shadows present in the scene. While traditional shape-from-shadow (SfS) algorithms reconstruct geometry from shadows, they assum
Externí odkaz:
http://arxiv.org/abs/2203.15946