Download presentation

Presentation is loading. Please wait.

Published byDominique Jenny Modified over 3 years ago

1
Multiview Reconstruction

2
Why More Than 2 Views? BaselineBaseline – Too short – low accuracy – Too long – matching becomes hard

3
Why More Than 2 Views? Ambiguity with 2 viewsAmbiguity with 2 views Camera 1 Camera 2 Camera 3

4
Trinocular Stereo Straightforward approach to eliminate bad correspondencesStraightforward approach to eliminate bad correspondences – Pick 2 views, find correspondences – For each matching pair, reconstruct 3D point – Project point into 3 rd image – If can’t find correspondence near predicted location, reject

5
Trinocular Stereo Trifocal geometry: relations between points in three camera viewsTrifocal geometry: relations between points in three camera views Trifocal tensor: analogue of essential matrixTrifocal tensor: analogue of essential matrix – 3x3x3 trilinear tensor (3D cube of numbers) – Given lines in 2 views, predict lines in the 3 rd

6
Multibaseline Stereo Slightly different algorithm for n cameras:Slightly different algorithm for n cameras: Pick one reference viewPick one reference view For each candidate depthFor each candidate depth – Compute sum of squared differences to all other views, assuming correct disparity for view Resolves ambiguities: only correct depths will “constructively interfere”Resolves ambiguities: only correct depths will “constructively interfere”

7
Multibaseline Stereo

8
[ Okutami & Kanade]

9
Multibaseline Stereo Reconstruction

10
Multibaseline Stereo

11
Problems with Multibaseline Stereo Have to pick a reference viewHave to pick a reference view OcclusionOcclusion – With many cameras / large baseline, occlusion becomes likely – Contributes incorrect values to error function

12
Volumetric Multiview Approaches Goal: find a model consistent with imagesGoal: find a model consistent with images “Model-centric” (vs. image-centric)“Model-centric” (vs. image-centric) Typically use discretized volume (voxel grid)Typically use discretized volume (voxel grid) For each voxel, compute occupied / free (for some algorithms, also color, etc.)For each voxel, compute occupied / free (for some algorithms, also color, etc.)

13
Photo Consistency Result: not necessarily correct sceneResult: not necessarily correct scene Many scenes produce the same imagesMany scenes produce the same images All scenes Photo-consistent scenes True scene Reconstructed scene

14
Silhouette Carving Find silhouettes in all imagesFind silhouettes in all images Exact version:Exact version: – Back-project all silhouettes, find intersection Binary Images

15
Silhouette Carving Find silhouettes in all imagesFind silhouettes in all images Exact version:Exact version: – Back-project all silhouettes, find intersection

16
Silhouette Carving Limit of silhouette carving is visual hull or line hullLimit of silhouette carving is visual hull or line hull Complement of lines that don’t intersect objectComplement of lines that don’t intersect object In general not the same as objectIn general not the same as object – Can’t recover “pits” in object Not the same as convex hullNot the same as convex hull

17
Silhouette Carving Discrete version:Discrete version: – Loop over all voxels in some volume – If projection into images lies inside all silhouettes, mark as occupied – Else mark as free

18
Silhouette Carving

19
Voxel Coloring Seitz and Dyer, 1997Seitz and Dyer, 1997 In addition to free / occupied, store color at each voxelIn addition to free / occupied, store color at each voxel Explicitly accounts for occlusionExplicitly accounts for occlusion

20
Voxel Coloring Basic idea: sweep through a voxel gridBasic idea: sweep through a voxel grid – Project each voxel into each image in which it is visible – If colors in images agree, mark voxel with color – Else, mark voxel as empty Agreement of colors based on comparing standard deviation of colors to thresholdAgreement of colors based on comparing standard deviation of colors to threshold

21
Voxel Coloring and Occlusion Problem: which voxels are visible?Problem: which voxels are visible? Solution, part 1: constrain camera viewsSolution, part 1: constrain camera views – When a voxel is considered, necessary occlusion information must be available – Sweep occluders before occludees – Constrain camera positions to allow this sweep

22
Voxel Coloring Sweep Order Layers SceneTraversal Seitz

23
Voxel Coloring Camera Positions Inward-looking Cameras above scene Outward-looking Cameras inside scene Seitz

24
Panoramic Depth Ordering Cameras oriented in many different directionsCameras oriented in many different directions Planar depth ordering does not applyPlanar depth ordering does not apply Seitz

25
Panoramic Depth Ordering Layers radiate outwards from cameras Seitz

26
Panoramic Depth Ordering Seitz Layers radiate outwards from cameras

27
Panoramic Depth Ordering Seitz Layers radiate outwards from cameras

28
Voxel Coloring and Occlusion Solution, part 2: per-image mask of which pixels have been usedSolution, part 2: per-image mask of which pixels have been used – Each pixel only used once – Mask filled in as sweep progresses

29
Image Acquisition Calibrated TurntableCalibrated Turntable 360° rotation (21 images)360° rotation (21 images) Selected Dinosaur Images Selected Flower Images Seitz

30
Voxel Coloring Results Dinosaur Reconstruction 72 K voxels colored 7.6 M voxels tested 7 min. to compute on a 250MHz SGI Flower Reconstruction 70 K voxels colored 7.6 M voxels tested 7 min. to compute on a 250MHz SGI Seitz

31
Voxel Coloring Results With texture: good resultsWith texture: good results Without texture: regions tend to “bulge out”Without texture: regions tend to “bulge out” – Voxels colored at earliest time at which projection into images is consistent – Model good for re-rendering: image will look correct for viewpoints near the original ones

32
Limitations of Voxel Coloring A view-independent depth order may not existA view-independent depth order may not exist Need more powerful general-case algorithmsNeed more powerful general-case algorithms – Unconstrained camera positions – Unconstrained scene geometry/topology pq

33
Space Carving Image 1 Image N …... Initialize to a volume V containing the true scene Repeat until convergence Choose a voxel on the current surface Carve if not photo-consistent Project to visible input images Kutulakos & Seitz

34
Multi-Pass Plane Sweep Faster alternative:Faster alternative: – Sweep plane in each of 6 principal directions – Consider cameras on only one side of plane – Repeat until convergence

35
Multi-Pass Plane Sweep True SceneReconstruction

36
Multi-Pass Plane Sweep

41
Space Carving Results: African Violet Input Image (1 of 45) Reconstruction ReconstructionReconstruction

42
Space Carving Results: Hand Input Image (1 of 100) Views of Reconstruction

Similar presentations

Presentation is loading. Please wait....

OK

875 Dynamic Scene Reconstruction

875 Dynamic Scene Reconstruction

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on event handling in javascript code Ppt on dth service free download Ppt on spiritual leadership institute Ppt on natural resources for class 9 download Ppt on 3d figures Ppt on friction in our daily life Ppt on environment and development Ppt on semi solid casting Free ppt on smart note taker working Ppt on online image editor