BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
arxiv(2023)
摘要
Implicit neural representations have become pivotal in robotic perception,
enabling robots to comprehend 3D environments from 2D images. Given a set of
camera poses and associated images, the models can be trained to synthesize
novel, unseen views. To successfully navigate and interact in dynamic settings,
robots require the understanding of their spatial surroundings driven by
unassisted reconstruction of 3D scenes and camera poses from real-time video
footage. Existing approaches like COLMAP and bundle-adjusting neural radiance
field methods take hours to days to process due to the high computational
demands of feature matching, dense point sampling, and training of a
multi-layer perceptron structure with a large number of parameters. To address
these challenges, we propose a framework called bundle-adjusting accelerated
neural graphics primitives (BAA-NGP) which leverages accelerated sampling and
hash encoding to expedite automatic pose refinement/estimation and 3D scene
reconstruction. Experimental results demonstrate 10 to 20 x speed improvement
compared to other bundle-adjusting neural radiance field methods without
sacrificing the quality of pose estimation. The github repository can be found
here https://github.com/IntelLabs/baa-ngp.
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