A semi-direct approach to structure from motion

被引:52
作者
Jin, HL [1 ]
Favaro, P
Soatto, S
机构
[1] Washington Univ, Dept Elect Engn, St Louis, MO 63130 USA
[2] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA
关键词
structure from motion; direct methods; extended Kalman filter; observability; tracking;
D O I
10.1007/s00371-003-0202-6
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The problem of structure from motion is often decomposed into two steps: feature correspondence and three-dimensional reconstruction. This separation often causes gross errors when establishing correspondence fails. Therefore, we advocate the necessity to integrate visual information not only in time (i.e. across different views), but also in space, by matching regions - rather than points - using explicit photometric deformation models. We present an algorithm that integrates image-feature tracking and three-dimensional motion estimation into a closed loop, while detecting and rejecting outlier regions that do not fit the model. Due to occlusions and the causal nature of our algorithm, a drift in the estimates accumulates over time. We describe a method to perform global registration of local estimates of motion and structure by matching the appearance of feature regions stored over long time periods. We use image intensities to construct a score function that takes into account changes in brightness and contrast. Our algorithm is recursive and suitable for real-time implementation.
引用
收藏
页码:377 / 394
页数:18
相关论文
共 30 条
[2]  
ALON J, 2000, IEEE COMPUT VISION P, V2, P550
[3]   RECURSIVE ESTIMATION OF MOTION, STRUCTURE, AND FOCAL LENGTH [J].
AZARBAYEJANI, A ;
PENTLAND, AP .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1995, 17 (06) :562-575
[4]  
Bartlett MS, 1956, INTRO STOCHASTIC PRO
[5]   ESTIMATION OF OBJECT MOTION PARAMETERS FROM NOISY IMAGES [J].
BROIDA, TJ ;
CHELLAPPA, R .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1986, 8 (01) :90-99
[6]   Optimal structure from motion: Local ambiguities and global estimates [J].
Chiuso, A ;
Brockett, R ;
Soatto, S .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2000, 39 (03) :195-228
[7]  
Dellaert F, 2000, PROC CVPR IEEE, P557, DOI 10.1109/CVPR.2000.854916
[8]  
DICKMANNS ED, 1988, MACH VISION APPL, V1, P241
[9]  
HANNA KJ, 1991, WORKSH VIS MOT, P156
[10]  
JIN H, 2000, P IEEE COMPUT VISION, V2, P778