371 research outputs found
Jigsaw: Learning to Assemble Multiple Fractured Objects
Automated assembly of 3D fractures is essential in orthopedics, archaeology,
and our daily life. This paper presents Jigsaw, a novel framework for
assembling physically broken 3D objects from multiple pieces. Our approach
leverages hierarchical features of global and local geometry to match and align
the fracture surfaces. Our framework consists of three components: (1) surface
segmentation to separate fracture and original parts, (2) multi-parts matching
to find correspondences among fracture surface points, and (3) robust global
alignment to recover the global poses of the pieces. We show how to jointly
learn segmentation and matching and seamlessly integrate feature matching and
rigidity constraints. We evaluate Jigsaw on the Breaking Bad dataset and
achieve superior performance compared to state-of-the-art methods. Our method
also generalizes well to diverse fracture modes, objects, and unseen instances.
To the best of our knowledge, this is the first learning-based method designed
specifically for 3D fracture assembly over multiple pieces.Comment: 17 pages, 9 figure
Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding
Human body orientation estimation (HBOE) is widely applied into various
applications, including robotics, surveillance, pedestrian analysis and
autonomous driving. Although many approaches have been addressing the HBOE
problem from specific under-controlled scenes to challenging in-the-wild
environments, they assume human instances are already detected and take a well
cropped sub-image as the input. This setting is less efficient and prone to
errors in real application, such as crowds of people. In the paper, we propose
a single-stage end-to-end trainable framework for tackling the HBOE problem
with multi-persons. By integrating the prediction of bounding boxes and
direction angles in one embedding, our method can jointly estimate the location
and orientation of all bodies in one image directly. Our key idea is to
integrate the HBOE task into the multi-scale anchor channel predictions of
persons for concurrently benefiting from engaged intermediate features.
Therefore, our approach can naturally adapt to difficult instances involving
low resolution and occlusion as in object detection. We validated the
efficiency and effectiveness of our method in the recently presented benchmark
MEBOW with extensive experiments. Besides, we completed ambiguous instances
ignored by the MEBOW dataset, and provided corresponding weak body-orientation
labels to keep the integrity and consistency of it for supporting studies
toward multi-persons. Our work is available at
\url{https://github.com/hnuzhy/JointBDOE}
Interfacial Properties of Bilayer and Trilayer Graphene on Metal Substrates
One popular approach to prepare graphene is to grow them on transition metal
substrates via chemical vapor deposition. By using the density functional
theory with dispersion correction, we systematically investigate for the first
time the interfacial properties of bilayer (BLG) and trilayer graphene (TLG) on
metal substrates. Three categories of interfacial structures are revealed. The
adsorption of B(T)LG on Al, Ag, Cu, Au, and Pt substrates is a weak
physisorption, but a band gap can be opened. The adsorption of B(T)LG on Ti,
Ni, and Co substrates is a strong chemisorption, and a stacking-insensitive
band gap is opened for the two uncontacted layers of TLG. The adsorption of
B(T)LG on Pd substrate is a weaker chemisorption, with a band gap opened for
the uncontacted layers. This fundamental study also helps for B(T)LG device
study due to inevitable graphene/metal contact.Comment: 1 table, 8 figure
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar
structures between graphs and they are matchable. However, these assumptions do
not align with real-world applications. This work addresses a more realistic
scenario where graphs exhibit diverse modes, requiring graph grouping before or
along with matching, a task termed mixture graph matching and clustering. We
introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free
algorithm that guarantees theoretical convergence through the
Minorize-Maximization framework and offers enhanced flexibility via relaxed
clustering. Building on M3C, we develop UM3C, an unsupervised model that
incorporates novel edge-wise affinity learning and pseudo label selection.
Extensive experimental results on public benchmarks demonstrate that our method
outperforms state-of-the-art graph matching and mixture graph matching and
clustering approaches in both accuracy and efficiency. Source code will be made
publicly available.Comment: 26 pages, 10 figure
What Makes Natural Scene Memorable?
Recent studies on image memorability have shed light on the visual features
that make generic images, object images or face photographs memorable. However,
a clear understanding and reliable estimation of natural scene memorability
remain elusive. In this paper, we provide an attempt to answer: "what exactly
makes natural scene memorable". Specifically, we first build LNSIM, a
large-scale natural scene image memorability database (containing 2,632 images
and memorability annotations). Then, we mine our database to investigate how
low-, middle- and high-level handcrafted features affect the memorability of
natural scene. In particular, we find that high-level feature of scene category
is rather correlated with natural scene memorability. Thus, we propose a deep
neural network based natural scene memorability (DeepNSM) predictor, which
takes advantage of scene category. Finally, the experimental results validate
the effectiveness of DeepNSM.Comment: Accepted to ACM MM Workshop
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