27:
154:(a) Reconstruct projection rays from the image points (b) For each projection ray R: (c) For each 3D contour: (c1) Estimate the nearest point P1 of ray R to a point on the contour (c2) if (n == 1) choose P1 as actual P for the point-line correspondence (c3) else compare P1 with P: if dist(P1, R) is smaller than dist(P, R) then choose P1 as new P (d) Use (P, R) as correspondence set. (e) Estimate pose with this correspondence set (f) Transform contours, goto (b)
30:
35:
33:
29:
28:
34:
32:
90:
in the 2D image are known. A common technique developed in 1995 for solving this is POSIT, where the 3D pose is estimated directly from the 3D model points and the 2D image points, and corrects the errors iteratively until a good estimate is found from a single image. Most implementations of POSIT
77:
pair, or an image sequence where, typically, the camera is moving with a known velocity. The objects which are considered can be rather general, including a living being or body parts, e.g., a head or hands. The methods which are used for determining the pose of an object, however, are usually
162:
Systems exist which use a database of an object at different rotations and translations to compare an input image against to estimate pose. These systems accuracy is limited to situations which are represented in their database of images, however the goal is to recognize a pose, rather than
206:: C++ package for (relative) pose estimation of three views. Includes cases of three corresponding points with lines at these points (as in feature positions and orientations, or curve points with tangents), and also for three corresponding points and one line correspondence.
131:
Starting with a 2D image, image points are extracted which correspond to corners in an image. The projection rays from the image points are reconstructed from the 2D points so that the 3D points, which must be incident with the reconstructed rays, can be determined.
31:
150:
The above algorithm does not account for images containing an object that is partially occluded. The following algorithm assumes that all contours are rigidly coupled, meaning the pose of one contour defines the pose of another contour.
147:(a) Reconstruct projection rays from the image points (b) Estimate the nearest point of each projection ray to a point on the 3D contour (c) Estimate the pose of the contour with the use of this correspondence set (d) goto (b)
122:
Given a 2D image of an object, and the camera that is calibrated with respect to a world coordinate system, it is also possible to find the pose which gives the 3D object in its object coordinate system. This works as follows.
477:
114:
is possible depending on the pose representation used. This approach is appropriate for applications where a 3D CAD model of a known object (or object category) is available.
86:
It is possible to estimate the 3D rotation and translation of a 3D object from a single 2D photo, if an approximate 3D model of the object is known and the
102:
a suitable distance measure with respect to the pose parameters. The distance measure is computed between the object in the photograph and the 3D
486:
352:
Srimal
Jayawardena and Marcus Hutter and Nathan Brewer (2011). "A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation".
430:
379:
547:
144:
algorithm. The main idea is to determine the correspondences between 2D image features and points on the 3D model curve.
455:
527:
197:
226:
196:
3D-2D correspondences of points with directions (vectors), or points at curves (point-tangents). The points can be
50:
is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a
326:
236:
78:
specific for a class of objects and cannot generally be expected to work well for other types of objects.
515:
413:
362:
542:
509:
179:
403:
Srimal
Jayawardena and Di Yang and Marcus Hutter (2011). "3D Model Assisted Image Segmentation".
141:
552:
408:
357:
107:
87:
74:
20:
91:
only work on non-coplanar points (in other words, it won't work with flat objects or planes).
73:
The image data from which the pose of an object is determined can be either a single image, a
221:
111:
63:
95:
8:
231:
216:
436:
385:
301:
351:
426:
405:
2011 International
Conference on Digital Image Computing: Techniques and Applications
375:
354:
2011 International
Conference on Digital Image Computing: Techniques and Applications
389:
305:
440:
418:
367:
293:
246:
241:
55:
402:
281:
40:
172:
536:
62:
where the pose or transformation of an object can be used for alignment of a
485:(Technical report). Boston University Computer Science Tech. Archived from
189:
422:
371:
266:
51:
330:
297:
99:
103:
203:
67:
59:
510:"Pose Estimation of 3D Free-form Contours in Conformal Geometry."
279:
140:
The algorithm for determining pose estimation is based on the
475:
183:
186:
library for 6DoF pose estimation from 3D-2D correspondences.
176:
16:
Process of determining spatial characteristics of objects
505:
Rosenhahn, B. "Foundations about 2D-3D Pose
Estimation."
192:, fast Matlab solver for 6DoF pose estimation from only
157:
98:CAD model over the photograph of a known object by
264:
516:"Estimating 3D Hand Pose from a Cluttered Image."
476:Vassilis Athitsos; Stan Sclarof (April 1, 2003).
81:
534:
479:Estimating 3D Hand Pose from a Cluttered Image
324:
117:
447:
282:"Model-based object pose in 25 lines of code"
280:Daniel F. Dementhon; Larry S. Davis (1995).
453:
456:"Foundations about 2D-3D Pose Estimation"
412:
361:
286:International Journal of Computer Vision
126:
25:
19:For broader coverage of this topic, see
327:"POSIT tutorial with OpenCV and OpenGL"
265:Javier Barandiaran (28 December 2017).
535:
200:attributed with feature directions.
13:
158:Estimating pose through comparison
94:Another approach is to register a
14:
564:
521:
70:, or manipulation of the object.
227:Articulated body pose estimation
499:
469:
396:
345:
318:
273:
258:
82:From an uncalibrated 2D camera
1:
252:
135:
528:Estimación de una Postura 3D
237:Homography (computer vision)
106:projection at a given pose.
7:
548:Geometry in computer vision
210:
166:
118:From a calibrated 2D camera
10:
569:
18:
66:models, identification,
142:iterative closest point
108:Perspective projection
44:
21:Pose (computer vision)
423:10.1109/DICTA.2011.17
372:10.1109/DICTA.2011.15
222:3D object recognition
127:Extracting 3D from 2D
112:orthogonal projection
64:computer-aided design
39:Pose estimation in a
38:
325:Javier Barandiaran.
88:corresponding points
217:Gesture recognition
407:. pp. 51–58.
356:. pp. 37–44.
298:10.1007/BF01450852
232:Camera calibration
48:3D pose estimation
45:
432:978-1-4577-2006-2
381:978-1-4577-2006-2
36:
560:
494:
493:
491:
484:
473:
467:
466:
464:
463:
454:Bodo Rosenhahn.
451:
445:
444:
416:
400:
394:
393:
365:
349:
343:
342:
340:
338:
329:. Archived from
322:
316:
315:
313:
312:
292:(1–2): 123–141.
277:
271:
270:
267:"POSIT tutorial"
262:
54:. It arises in
37:
568:
567:
563:
562:
561:
559:
558:
557:
543:Computer vision
533:
532:
524:
502:
497:
489:
482:
474:
470:
461:
459:
452:
448:
433:
414:10.1.1.751.8774
401:
397:
382:
363:10.1.1.766.3931
350:
346:
336:
334:
333:on 20 June 2010
323:
319:
310:
308:
278:
274:
263:
259:
255:
247:Pose estimation
242:Trifocal tensor
213:
169:
160:
155:
148:
138:
129:
120:
84:
56:computer vision
26:
24:
17:
12:
11:
5:
566:
556:
555:
550:
545:
531:
530:
523:
522:External links
520:
519:
518:
512:
508:Rosenhahn, B.
506:
501:
498:
496:
495:
492:on 2019-07-31.
468:
446:
431:
395:
380:
344:
317:
272:
256:
254:
251:
250:
249:
244:
239:
234:
229:
224:
219:
212:
209:
208:
207:
201:
187:
168:
165:
163:determine it.
159:
156:
153:
146:
137:
134:
128:
125:
119:
116:
83:
80:
41:motion capture
15:
9:
6:
4:
3:
2:
565:
554:
553:Robot control
551:
549:
546:
544:
541:
540:
538:
529:
526:
525:
517:
514:Athitsos, V.
513:
511:
507:
504:
503:
488:
481:
480:
472:
457:
450:
442:
438:
434:
428:
424:
420:
415:
410:
406:
399:
391:
387:
383:
377:
373:
369:
364:
359:
355:
348:
332:
328:
321:
307:
303:
299:
295:
291:
287:
283:
276:
268:
261:
257:
248:
245:
243:
240:
238:
235:
233:
230:
228:
225:
223:
220:
218:
215:
214:
205:
202:
199:
195:
191:
190:diffgeom2pose
188:
185:
181:
178:
174:
171:
170:
164:
152:
145:
143:
133:
124:
115:
113:
109:
105:
101:
97:
92:
89:
79:
76:
71:
69:
65:
61:
57:
53:
49:
42:
22:
500:Bibliography
487:the original
478:
471:
460:. Retrieved
449:
404:
398:
353:
347:
335:. Retrieved
331:the original
320:
309:. Retrieved
289:
285:
275:
260:
193:
161:
149:
139:
130:
121:
93:
85:
75:stereo image
72:
47:
46:
458:. CV Online
537:Categories
462:2008-06-09
311:2010-05-29
253:References
136:Pseudocode
100:optimizing
409:CiteSeerX
358:CiteSeerX
269:. OpenCV.
104:CAD model
390:17296505
306:14501637
211:See also
167:Software
68:grasping
60:robotics
441:1665253
52:3D scan
439:
429:
411:
388:
378:
360:
337:29 May
304:
173:posest
43:system
490:(PDF)
483:(PDF)
437:S2CID
386:S2CID
302:S2CID
204:MINUS
427:ISBN
376:ISBN
339:2010
198:SIFT
175:, a
419:doi
368:doi
294:doi
194:two
184:C++
177:GPL
110:or
58:or
539::
435:.
425:.
417:.
384:.
374:.
366:.
300:.
290:15
288:.
284:.
96:3D
465:.
443:.
421::
392:.
370::
341:.
314:.
296::
182:/
180:C
23:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.