TY - GEN
T1 - In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces
AU - Xiong, Jinhui
AU - Heidrich, Wolfgang
N1 - KAUST Repository Item: Exported on 2022-03-09
PY - 2021
Y1 - 2021
N2 - We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water. We propose a novel differentiable framework, which, to our knowledge, is the first single-camera solution that is capable of simultaneously retrieving the structure of dynamic water surfaces and static underwater scene geometry in the wild. This framework integrates ray casting of Snell’s law at the refractive interface, multi-view triangulation and specially designed loss functions.Our method is calibration-free, and thus it is easy to collect data outdoors in uncontrolled environments. Experimental results show that our method is able to realize robust and quality reconstructions on a variety of scenes, both in a laboratory environment and in the wild, and even in a salt water environment. We believe the method is promising for applications in surveying and environmental monitoring.
AB - We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water. We propose a novel differentiable framework, which, to our knowledge, is the first single-camera solution that is capable of simultaneously retrieving the structure of dynamic water surfaces and static underwater scene geometry in the wild. This framework integrates ray casting of Snell’s law at the refractive interface, multi-view triangulation and specially designed loss functions.Our method is calibration-free, and thus it is easy to collect data outdoors in uncontrolled environments. Experimental results show that our method is able to realize robust and quality reconstructions on a variety of scenes, both in a laboratory environment and in the wild, and even in a salt water environment. We believe the method is promising for applications in surveying and environmental monitoring.
UR - http://hdl.handle.net/10754/675748
UR - https://ieeexplore.ieee.org/document/9710376/
U2 - 10.1109/ICCV48922.2021.01233
DO - 10.1109/ICCV48922.2021.01233
M3 - Conference contribution
SN - 978-1-6654-2813-2
BT - 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
PB - IEEE
ER -