29 research outputs found
Carving from Ray-Tracing Constraints: IRT-Carving
We present a new algorithm for improving an available
(conservative) estimate of the shape of an object using constraints
from ray-tracing. In particular, we exploit incoherences
between the lit portions of the object - detected on
a set of acquired images - and the shadows that the current
estimate casts on itself. Whenever a contradiction is
found the current estimate is modified in order to remove
the inconsistency. Sufficient conditions for the correctness
of the algorithm and a discussion of their validity are provided.
Finally, we describe a simple implementation of the
method and present some preliminary experimental results
from computer simulations
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
Recovering the 3D representation of an object from single-view or multi-view
RGB images by deep neural networks has attracted increasing attention in the
past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural
networks (RNNs) to fuse multiple feature maps extracted from input images
sequentially. However, when given the same set of input images with different
orders, RNN-based approaches are unable to produce consistent reconstruction
results. Moreover, due to long-term memory loss, RNNs cannot fully exploit
input images to refine reconstruction results. To solve these problems, we
propose a novel framework for single-view and multi-view 3D reconstruction,
named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse
3D volume from each input image. Then, a context-aware fusion module is
introduced to adaptively select high-quality reconstructions for each part
(e.g., table legs) from different coarse 3D volumes to obtain a fused 3D
volume. Finally, a refiner further refines the fused 3D volume to generate the
final output. Experimental results on the ShapeNet and Pix3D benchmarks
indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large
margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in
terms of backward inference time. The experiments on ShapeNet unseen 3D
categories have shown the superior generalization abilities of our method.Comment: ICCV 201
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
3D reconstruction from a single image is a key problem in multiple
applications ranging from robotic manipulation to augmented reality. Prior
methods have tackled this problem through generative models which predict 3D
reconstructions as voxels or point clouds. However, these methods can be
computationally expensive and miss fine details. We introduce a new
differentiable layer for 3D data deformation and use it in DeformNet to learn a
model for 3D reconstruction-through-deformation. DeformNet takes an image
input, searches the nearest shape template from a database, and deforms the
template to match the query image. We evaluate our approach on the ShapeNet
dataset and show that - (a) the Free-Form Deformation layer is a powerful new
building block for Deep Learning models that manipulate 3D data (b) DeformNet
uses this FFD layer combined with shape retrieval for smooth and
detail-preserving 3D reconstruction of qualitatively plausible point clouds
with respect to a single query image (c) compared to other state-of-the-art 3D
reconstruction methods, DeformNet quantitatively matches or outperforms their
benchmarks by significant margins. For more information, visit:
https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in
single-view 3D reconstruction suggests that these models perform non-trivial
reasoning about the 3D structure of the output space. However, recent work has
challenged this belief, showing that complex encoder-decoder architectures
perform similarly to nearest-neighbor baselines or simple linear decoder models
that exploit large amounts of per category data in standard benchmarks. On the
other hand settings where 3D shape must be inferred for new categories with few
examples are more natural and require models that generalize about shapes. In
this work we demonstrate experimentally that naive baselines do not apply when
the goal is to learn to reconstruct novel objects using very few examples, and
that in a \emph{few-shot} learning setting, the network must learn concepts
that can be applied to new categories, avoiding rote memorization. To address
deficiencies in existing approaches to this problem, we propose three
approaches that efficiently integrate a class prior into a 3D reconstruction
model, allowing to account for intra-class variability and imposing an implicit
compositional structure that the model should learn. Experiments on the popular
ShapeNet database demonstrate that our method significantly outperform existing
baselines on this task in the few-shot setting
Weakly supervised 3D Reconstruction with Adversarial Constraint
Supervised 3D reconstruction has witnessed a significant progress through the
use of deep neural networks. However, this increase in performance requires
large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D
supervision as an alternative for expensive 3D CAD annotation. Specifically, we
use foreground masks as weak supervision through a raytrace pooling layer that
enables perspective projection and backpropagation. Additionally, since the 3D
reconstruction from masks is an ill posed problem, we propose to constrain the
3D reconstruction to the manifold of unlabeled realistic 3D shapes that match
mask observations. We demonstrate that learning a log-barrier solution to this
constrained optimization problem resembles the GAN objective, enabling the use
of existing tools for training GANs. We evaluate and analyze the manifold
constrained reconstruction on various datasets for single and multi-view
reconstruction of both synthetic and real images
Surface Reconstruction and Evolution from Multiple Views
Applications like 3D Telepresence necessitate faithful 3D surface reconstruction
of the object and 3D data compression in both spatial and
temporal domains. This makes us feel immersed in virtual environments
there by making 3D Telepresence a powerful tool in many applications.
Hence 3D surface reconstruction and 3D compression are two challenging
problems which are addressed in this thesis
Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System
The location determination in obstructed area can be very challenging
especially if Global Positioning System are blocked. Users will find it
difficult to navigate directly on-site in such condition, especially indoor car
park lot or obstructed environment. Sometimes, it needs to combine with other
sensors and positioning methods in order to determine the location with more
intelligent, reliable and ubiquity. By using ubiquitous positioning in mobile
navigation system, it is a promising ubiquitous location technique in a mobile
phone since as it is a familiar personal electronic device for many people.
However, as research on ubiquitous positioning systems goes beyond basic
methods there is an increasing need for better comparison of proposed
ubiquitous positioning systems. System developers are also lacking of good
frameworks for understanding different options during building ubiquitous
positioning systems. This paper proposes taxonomy to address both of these
problems. The proposed taxonomy has been constructed from a literature study of
papers and articles on positioning estimation that can be used to determine
location everywhere on mobile navigation system. For researchers the taxonomy
can also be used as an aid for scoping out future research in the area of
ubiquitous positioning.Comment: 15 Pages, 3 figure
An Investigation of Object Shadows Utilization In 3D Shape Re-Construction Using Inexpensive Equipment
An approach for automatic 3D object re-construction using its shadow ispresented. The approach investigates the use of information inherited by thegenerated object shadows to re-construct the object geometry. An algorithm isdeveloped that make use of object height information for the directions associatedwith the incident light and the generated object shadows, hence, acquired heightfeatures represents the object features that have actually obstructed the incidentlight. The technique is tested using objects of different shapes. Close to realmeasurements are gained and the overall accuracy of the system is found to bewithin 0.75 mm using the adopted imaging hardware and setup. Obtained resultsconfirmed the validity of the proposed approach