9. 3D Shapeの表現
9
Figure from “Occupancy Networks: Learning 3D Reconstruction in Function Space”
Voxel Point Cloud Mesh
+Simple
-Cubic Memory
-Manhattan world
+Fast and Easy
-No connectivity
-Lossy Postprocessing
+Natural
-Require Template
(topology)
-Self-intersections
10. 3D Shapeの表現
10
Voxel Point Cloud Mesh Implicit Function
+Infinite Resolution
+Arbitrary Topologies
+Watertight Meshes
Figure from “Occupancy Networks: Learning 3D Reconstruction in Function Space”
+Simple
-Cubic Memory
-Manhattan world
+Fast and Easy
-No connectivity
-Lossy Postprocessing
+Natural
-Require Template
(topology)
-Self-intersections
11. 3D Shapeの表現
11
陰関数(Implicit Function)をDeep Learningで表現
(いずれもCVPR2019)
IM-NET
Learning Implicit Fields for Generative Shape Modeling
OccNET
Occupancy Networks: Learning 3D Reconstruction in
Function Space
DeepSDF
DeepSDF: Learning Continuous Signed Distance
Functions for Shape Representation
https://www.slideshare.net/takmin/20190706cvpr20193dshaperepresentation-153989245
55. Auto-encoding 3D Shapes
55
パーツへ分割する既存研究と比較
Volumetric Primitives (VP)
Tulsiani, S., Su, H., Guibas, L. J., Efros,A.A., & Malik, J. (2017). Learning
shape abstractions by assembling volumetric primitives. In Conference on
ComputerVision and Pattern Recognition.
3D ShapeをPrimitive Shapeの集合で表現
Super Quadrics (SQ)
Paschalidou, D., Ulusoy,A. O., & Geiger,A. (2019). Superquadrics revisited:
Learning 3D shape parsing beyond cuboids. IEEE Conference on Computer
Vision and Pattern Recognition, 2019-June, 10336–10345.
3D Shapeを超楕円体 (Super Quadrics)の集合で表現
Branched Auto Encoders (BAE)
Chen, Z.,Yin, K., Fisher, M., Chaudhuri, S., & Zhang, H. (2019). BAE-NET :
Branched Autoencoder for Shape Co-Segmentation. In International
Conference on ComputerVision.
3D Shapeを陰関数で表現したパーツの集合で表現
61. Single View Reconstruction (SVR)
61
以下の手法と比較
Atlasnet
Groueix,T., Fisher, M., Kim,V. G., Russell, B. C., & Aubry, M. (2018).A
Papier-Mache Approach to Learning 3D Surface Generation. In
Conference on ComputerVision and Pattern Recognition.
OccNet
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger,A.
(2019). Occupancy Networks: Learning 3D Reconstruction in
Function Space. Conference on ComputerVision and Pattern Recognition.
IM-NET
Chen, Z. (2019). Learning Implicit Fields for Generative Shape
Modeling. Conference on ComputerVision and Pattern Recognition.