137,320 research outputs found
Beyond Gr\"obner Bases: Basis Selection for Minimal Solvers
Many computer vision applications require robust estimation of the underlying
geometry, in terms of camera motion and 3D structure of the scene. These robust
methods often rely on running minimal solvers in a RANSAC framework. In this
paper we show how we can make polynomial solvers based on the action matrix
method faster, by careful selection of the monomial bases. These monomial bases
have traditionally been based on a Gr\"obner basis for the polynomial ideal.
Here we describe how we can enumerate all such bases in an efficient way. We
also show that going beyond Gr\"obner bases leads to more efficient solvers in
many cases. We present a novel basis sampling scheme that we evaluate on a
number of problems
Hybrid Scene Compression for Visual Localization
Localizing an image wrt. a 3D scene model represents a core task for many
computer vision applications. An increasing number of real-world applications
of visual localization on mobile devices, e.g., Augmented Reality or autonomous
robots such as drones or self-driving cars, demand localization approaches to
minimize storage and bandwidth requirements. Compressing the 3D models used for
localization thus becomes a practical necessity. In this work, we introduce a
new hybrid compression algorithm that uses a given memory limit in a more
effective way. Rather than treating all 3D points equally, it represents a
small set of points with full appearance information and an additional, larger
set of points with compressed information. This enables our approach to obtain
a more complete scene representation without increasing the memory
requirements, leading to a superior performance compared to previous
compression schemes. As part of our contribution, we show how to handle
ambiguous matches arising from point compression during RANSAC. Besides
outperforming previous compression techniques in terms of pose accuracy under
the same memory constraints, our compression scheme itself is also more
efficient. Furthermore, the localization rates and accuracy obtained with our
approach are comparable to state-of-the-art feature-based methods, while using
a small fraction of the memory.Comment: Published at CVPR 201
Efficient Spatially Adaptive Convolution and Correlation
Fast methods for convolution and correlation underlie a variety of
applications in computer vision and graphics, including efficient filtering,
analysis, and simulation. However, standard convolution and correlation are
inherently limited to fixed filters: spatial adaptation is impossible without
sacrificing efficient computation. In early work, Freeman and Adelson have
shown how steerable filters can address this limitation, providing a way for
rotating the filter as it is passed over the signal. In this work, we provide a
general, representation-theoretic, framework that allows for spatially varying
linear transformations to be applied to the filter. This framework allows for
efficient implementation of extended convolution and correlation for
transformation groups such as rotation (in 2D and 3D) and scale, and provides a
new interpretation for previous methods including steerable filters and the
generalized Hough transform. We present applications to pattern matching, image
feature description, vector field visualization, and adaptive image filtering
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation
Head generation with diverse identities is an important task in computer
vision and computer graphics, widely used in multimedia applications. However,
current full head generation methods require a large number of 3D scans or
multi-view images to train the model, resulting in expensive data acquisition
cost. To address this issue, we propose Head3D, a method to generate full 3D
heads with limited multi-view images. Specifically, our approach first extracts
facial priors represented by tri-planes learned in EG3D, a 3D-aware generative
model, and then proposes feature distillation to deliver the 3D frontal faces
into complete heads without compromising head integrity. To mitigate the domain
gap between the face and head models, we present dual-discriminators to guide
the frontal and back head generation, respectively. Our model achieves
cost-efficient and diverse complete head generation with photo-realistic
renderings and high-quality geometry representations. Extensive experiments
demonstrate the effectiveness of our proposed Head3D, both qualitatively and
quantitatively
NOFA: NeRF-based One-shot Facial Avatar Reconstruction
3D facial avatar reconstruction has been a significant research topic in
computer graphics and computer vision, where photo-realistic rendering and
flexible controls over poses and expressions are necessary for many related
applications. Recently, its performance has been greatly improved with the
development of neural radiance fields (NeRF). However, most existing NeRF-based
facial avatars focus on subject-specific reconstruction and reenactment,
requiring multi-shot images containing different views of the specific subject
for training, and the learned model cannot generalize to new identities,
limiting its further applications. In this work, we propose a one-shot 3D
facial avatar reconstruction framework that only requires a single source image
to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking
generalization ability and missing multi-view information, we leverage the
generative prior of 3D GAN and develop an efficient encoder-decoder network to
reconstruct the canonical neural volume of the source image, and further
propose a compensation network to complement facial details. To enable
fine-grained control over facial dynamics, we propose a deformation field to
warp the canonical volume into driven expressions. Through extensive
experimental comparisons, we achieve superior synthesis results compared to
several state-of-the-art methods
Adaptive graph formulation for 3D shape representation
3D shape recognition has attracted a great interest in computer vision due to its large number of important and exciting applications. This has led to exploring a variety of approaches to develop more efficient 3D analysis methods. However, current works take into account descriptions of global shape to generate models, ignoring small differences causing the problem of mismatching, especially for high similarity shapes. The present paper, therefore, proposes a new approach to represent 3D shapes based on graph formulation and its spectral analysis which can accurately represent local details and small surface variations. An adaptive graph is generated over the 3D shape to characterise the topology of the shape, followed by extracting a set of discriminating features to characterise the shape structure to train a classifier. The evaluation results show that the proposed method exceeds the state-of-the-art performance by 4% for a challenging dataset
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