ausblenden:
Schlagwörter:
Computer Science, Graphics, cs.GR,Computer Science, Computer Vision and Pattern Recognition, cs.CV
Zusammenfassung:
Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed
reality and robotic applications. However, scalability brings challenges of
drift in pose estimation, introducing significant errors in the accumulated
model. Approaches often require hours of offline processing to globally correct
model errors. Recent online methods demonstrate compelling results, but suffer
from: (1) needing minutes to perform online correction preventing true
real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation
resulting in many tracking failures; or (3) supporting only unstructured
point-based representations, which limit scan quality and applicability. We
systematically address these issues with a novel, real-time, end-to-end
reconstruction framework. At its core is a robust pose estimation strategy,
optimizing per frame for a global set of camera poses by considering the
complete history of RGB-D input with an efficient hierarchical approach. We
remove the heavy reliance on temporal tracking, and continually localize to the
globally optimized frames instead. We contribute a parallelizable optimization
framework, which employs correspondences based on sparse features and dense
geometric and photometric matching. Our approach estimates globally optimized
(i.e., bundle adjusted) poses in real-time, supports robust tracking with
recovery from gross tracking failures (i.e., relocalization), and re-estimates
the 3D model in real-time to ensure global consistency; all within a single
framework. Our approach outperforms state-of-the-art online systems with
quality on par to offline methods, but with unprecedented speed and scan
completeness. Our framework leads to a comprehensive online scanning solution
for large indoor environments, enabling ease of use and high-quality results.
