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Abstract

This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D convolutional network which is achieved by encoding the camera parameters together with the images in a 3D voxel representation. We evaluate SurfaceNet on the large-scale DTU benchmark. Code is available in https://github.com/mjiUST/SurfaceNet.
International Conference on
Computer Vision 2017
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, Lu Fang
mji@connect.ust.hk
Contribution
Related Works
The first end-to-end learning frame work for MVS:
network directly learns the photo-consistency and
geometric relationship.
new 3D voxel representation encoding the camera
poses.
MVS takes multiple images with camera poses as inputs.
Standard pipelines and drawbacks:
Volumetric methods [5]: Manually designed graph-based
cost functions.
Depth map fusion methods [1,2,3,4,6]: Engineered
processing steps.
Reference Ours Camp [1]
Furu [2] Tola[3] Gipuma [4]
SurfaceNet: 2 views SurfaceNet: N views
Evaluation
Problem: how to embed the camera parameter into an
end-to-end network?
Solution: propose a 3D voxel representation for each
view:colored voxel cube (CVC).
1. Scene overlapping volumes voxel grid
2. Each pixel corresponds to a voxel ray.
3. Colorize different voxels on the same voxel ray as the
same color.
3D SurfaceNet:
1. takes 2 colored voxel cubes from 2 different views as
input, with size of (s,s,s). s=32 for training, s can vary
during inference owing to the fully ConvNet.
2. predicts for each voxel a binary occupancy attribute
indicating if the voxel is on the surface or not.
3. consists of multiple network layer groups: 𝑙𝑖,s𝑖each of
which includes several convolutional layers and pooling
layers.
[1] N. D. Campbell, et al. Using multiple hypotheses to improve depth-maps for multi-view stereo.
ICCV 2008.
[2] Y. Furukawa, et al.Accurate, dense, and robust mul-tiview stereopsis. PAMI 2010.
[3] E. Tola, et al. Efficient large-scale multi-view stereo for ultra high-resolution image sets. MVA
2012.
[4] S. Galliani, et al. Massively parallel multiview stereopsis by surface normal diffusion. ICCV 2015.
[5] http://www.ctralie.com/PrincetonUGRAD/Projects/SpaceCarving/
[6] https://www.cse.wustl.edu/~furukawa/newimages/fnt_mvs.png
[8] Aanæs, Henrik, et al. Large-scale data for multiple-view stereopsis. IJCV 2016.
[9] S. M. Seitz, et al. A comparison and evaluation of multi-view stereo reconstruction algorithms.
CVPR 2006.
Problem: fusing all predictions of all the view pairs is
not feasible. Since 50 views 1000+ view pairs.
Solution: only use the valuable view pairs.
relative importance 𝑤for each view pair based on
baseline and the image appearance on both views
(left) Randomly select 5 view pairs out of 1000+.
(Right) Select 5 view pairs with top 𝑤value.
DTU dataset [8]:evaluate randomly selected 22
models. The left models are used for training.
Comparable results:
How many view pairs are needed:
Another dataset: use only 6
images of the dinoSparseRing
model in the Middlebury dataset.
training:
Crop volumes from subset of the scenes from the DTU
dataset [8]. takes 2 colored voxel cubes from 2 different
views as input.

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Large-scale data for multiple-view stereopsis
  • Aanae S
  • Henrik
Aanae s, Henrik, et al. Large-scale data for multiple-view stereopsis. IJCV 2016.