1,364 research outputs found
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
Flow-based generative models have highly desirable properties like exact
log-likelihood evaluation and exact latent-variable inference, however they are
still in their infancy and have not received as much attention as alternative
generative models. In this paper, we introduce C-Flow, a novel conditioning
scheme that brings normalizing flows to an entirely new scenario with great
possibilities for multi-modal data modeling. C-Flow is based on a parallel
sequence of invertible mappings in which a source flow guides the target flow
at every step, enabling fine-grained control over the generation process. We
also devise a new strategy to model unordered 3D point clouds that, in
combination with the conditioning scheme, makes it possible to address 3D
reconstruction from a single image and its inverse problem of rendering an
image given a point cloud. We demonstrate our conditioning method to be very
adaptable, being also applicable to image manipulation, style transfer and
multi-modal image-to-image mapping in a diversity of domains, including RGB
images, segmentation maps, and edge masks
PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows
Point cloud upsampling aims to generate dense point clouds from given sparse
ones, which is a challenging task due to the irregular and unordered nature of
point sets. To address this issue, we present a novel deep learning-based
model, called PU-Flow, which incorporates normalizing flows and weight
prediction techniques to produce dense points uniformly distributed on the
underlying surface. Specifically, we exploit the invertible characteristics of
normalizing flows to transform points between Euclidean and latent spaces and
formulate the upsampling process as ensemble of neighbouring points in a latent
space, where the ensemble weights are adaptively learned from local geometric
context. Extensive experiments show that our method is competitive and, in most
test cases, it outperforms state-of-the-art methods in terms of reconstruction
quality, proximity-to-surface accuracy, and computation efficiency. The source
code will be publicly available at https://github.com/unknownue/pu-flow
Discrete Point Flow Networks for Efficient Point Cloud Generation
Generative models have proven effective at modeling 3D shapes and their
statistical variations. In this paper we investigate their application to point
clouds, a 3D shape representation widely used in computer vision for which,
however, only few generative models have yet been proposed. We introduce a
latent variable model that builds on normalizing flows with affine coupling
layers to generate 3D point clouds of an arbitrary size given a latent shape
representation. To evaluate its benefits for shape modeling we apply this model
for generation, autoencoding, and single-view shape reconstruction tasks. We
improve over recent GAN-based models in terms of most metrics that assess
generation and autoencoding. Compared to recent work based on continuous flows,
our model offers a significant speedup in both training and inference times for
similar or better performance. For single-view shape reconstruction we also
obtain results on par with state-of-the-art voxel, point cloud, and mesh-based
methods.Comment: In ECCV'2
Flow-based GAN for 3D Point Cloud Generation from a Single Image
Generating a 3D point cloud from a single 2D image is of great importance for
3D scene understanding applications. To reconstruct the whole 3D shape of the
object shown in the image, the existing deep learning based approaches use
either explicit or implicit generative modeling of point clouds, which,
however, suffer from limited quality. In this work, we aim to alleviate this
issue by introducing a hybrid explicit-implicit generative modeling scheme,
which inherits the flow-based explicit generative models for sampling point
clouds with arbitrary resolutions while improving the detailed 3D structures of
point clouds by leveraging the implicit generative adversarial networks (GANs).
We evaluate on the large-scale synthetic dataset ShapeNet, with the
experimental results demonstrating the superior performance of the proposed
method. In addition, the generalization ability of our method is demonstrated
by performing on cross-category synthetic images as well as by testing on real
images from PASCAL3D+ dataset.Comment: 13 pages, 5 figures, accepted to BMVC202
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