78,466 research outputs found
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the traditional 2D to 3D results in high computational overhead with little
additional benefit as most of the geometry information is contained on the
surface boundary. Here we study the problem of directly generating the 3D shape
surface of rigid and non-rigid shapes using deep convolutional neural networks.
We develop a procedure to create consistent `geometry images' representing the
shape surface of a category of 3D objects. We then use this consistent
representation for category-specific shape surface generation from a parametric
representation or an image by developing novel extensions of deep residual
networks for the task of geometry image generation. Our experiments indicate
that our network learns a meaningful representation of shape surfaces allowing
it to interpolate between shape orientations and poses, invent new shape
surfaces and reconstruct 3D shape surfaces from previously unseen images.Comment: CVPR 2017 pape
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that
learns the underlying structure of voxelized 3D shapes in an unsupervised
fashion. Through the use of skip-connections, our model can successfully learn
and infer a latent, hierarchical representation of objects. Furthermore,
realistic 3D objects can be easily generated by sampling the VSL's latent
probabilistic manifold. We show that our generative model can be trained
end-to-end from 2D images to perform single image 3D model retrieval.
Experiments show, both quantitatively and qualitatively, the improved
generalization of our proposed model over a range of tasks, performing better
or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D
Vision (3DV), 201
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
- …