14,715 research outputs found
Learning to Synthesize a 4D RGBD Light Field from a Single Image
We present a machine learning algorithm that takes as input a 2D RGB image
and synthesizes a 4D RGBD light field (color and depth of the scene in each ray
direction). For training, we introduce the largest public light field dataset,
consisting of over 3300 plenoptic camera light fields of scenes containing
flowers and plants. Our synthesis pipeline consists of a convolutional neural
network (CNN) that estimates scene geometry, a stage that renders a Lambertian
light field using that geometry, and a second CNN that predicts occluded rays
and non-Lambertian effects. Our algorithm builds on recent view synthesis
methods, but is unique in predicting RGBD for each light field ray and
improving unsupervised single image depth estimation by enforcing consistency
of ray depths that should intersect the same scene point. Please see our
supplementary video at https://youtu.be/yLCvWoQLnmsComment: International Conference on Computer Vision (ICCV) 201
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
Multi-View Stereo with Single-View Semantic Mesh Refinement
While 3D reconstruction is a well-established and widely explored research
topic, semantic 3D reconstruction has only recently witnessed an increasing
share of attention from the Computer Vision community. Semantic annotations
allow in fact to enforce strong class-dependent priors, as planarity for ground
and walls, which can be exploited to refine the reconstruction often resulting
in non-trivial performance improvements. State-of-the art methods propose
volumetric approaches to fuse RGB image data with semantic labels; even if
successful, they do not scale well and fail to output high resolution meshes.
In this paper we propose a novel method to refine both the geometry and the
semantic labeling of a given mesh. We refine the mesh geometry by applying a
variational method that optimizes a composite energy made of a state-of-the-art
pairwise photo-metric term and a single-view term that models the semantic
consistency between the labels of the 3D mesh and those of the segmented
images. We also update the semantic labeling through a novel Markov Random
Field (MRF) formulation that, together with the classical data and smoothness
terms, takes into account class-specific priors estimated directly from the
annotated mesh. This is in contrast to state-of-the-art methods that are
typically based on handcrafted or learned priors. We are the first, jointly
with the very recent and seminal work of [M. Blaha et al arXiv:1706.08336,
2017], to propose the use of semantics inside a mesh refinement framework.
Differently from [M. Blaha et al arXiv:1706.08336, 2017], which adopts a more
classical pairwise comparison to estimate the flow of the mesh, we apply a
single-view comparison between the semantically annotated image and the current
3D mesh labels; this improves the robustness in case of noisy segmentations.Comment: {\pounds}D Reconstruction Meets Semantic, ICCV worksho
A Joint 3D-2D based Method for Free Space Detection on Roads
In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201
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