BundleFusion
Stanford University · Max Planck Institute for Informatics · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 971.63
- Percentile
- 100%
- References
- 63
Authors
5Topics & keywords
- Computer science
- Bundle adjustment
- Artificial intelligence
- Scalability
- Computer vision
- Frame (networking)
- Robustness (evolution)
- Key frame