Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Gao, Alexander"'
Vector fields are widely used to represent and model flows for many science and engineering applications. This paper introduces a novel neural network architecture for learning tangent vector fields that are intrinsically defined on manifold surfaces
Externí odkaz:
http://arxiv.org/abs/2406.09648
Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of
Externí odkaz:
http://arxiv.org/abs/2309.04581
We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. To decouple the learning of underlying scene geometry from dynamic motion, we represent the scene as a time-invariant signed
Externí odkaz:
http://arxiv.org/abs/2210.12352
Autor:
Han, Wenyu, Feng, Chen, Wu, Haoran, Gao, Alexander, Jordana, Armand, Liu, Dong, Pinto, Lerrel, Righetti, Ludovic
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major robot vision and learning challenge is how to exactly achieve the desig
Externí odkaz:
http://arxiv.org/abs/2103.16732