349 research outputs found
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
This paper presents PolyDiffuse, a novel structured reconstruction algorithm
that transforms visual sensor data into polygonal shapes with Diffusion Models
(DM), an emerging machinery amid exploding generative AI, while formulating
reconstruction as a generation process conditioned on sensor data. The task of
structured reconstruction poses two fundamental challenges to DM: 1) A
structured geometry is a ``set'' (e.g., a set of polygons for a floorplan
geometry), where a sample of elements has different but equivalent
representations, making the denoising highly ambiguous; and 2) A
``reconstruction'' task has a single solution, where an initial noise needs to
be chosen carefully, while any initial noise works for a generation task. Our
technical contribution is the introduction of a Guided Set Diffusion Model
where 1) the forward diffusion process learns guidance networks to control
noise injection so that one representation of a sample remains distinct from
its other permutation variants, thus resolving denoising ambiguity; and 2) the
reverse denoising process reconstructs polygonal shapes, initialized and
directed by the guidance networks, as a conditional generation process subject
to the sensor data. We have evaluated our approach for reconstructing two types
of polygonal shapes: floorplan as a set of polygons and HD map for autonomous
cars as a set of polylines. Through extensive experiments on standard
benchmarks, we demonstrate that PolyDiffuse significantly advances the current
state of the art and enables broader practical applications.Comment: Project page: https://poly-diffuse.github.io
Analysis of the Properties of Adjoint Equations and Accuracy Verification of Adjoint Model Based on FVM
There are two different approaches on how to formulate adjoint numerical model (ANM). Aiming at the disputes arising from the construction methods of ANM, the differences between nonlinear shallow water equation and its adjoint equation are analyzed; the hyperbolicity and homogeneity of the adjoint equation are discussed. Then, based on unstructured meshes and finite volume method, a new adjoint model was advanced by getting numerical model of the adjoint equations directly. Using a gradient check, the correctness of the adjoint model was verified. The results of twin experiments to invert the bottom friction coefficient (Manning’s roughness coefficient) indicate that the adjoint model can extract the observation information and produce good quality inversion. The reason of disputes about construction methods of ANM is also discussed in the paper
Understanding the Adoption of Smart Community Services: Perceived Usefulness, Enjoyment, and Affective Community Commitment
Smart community is an emerging form of community that provides various convenient services (smart community services (SCS)) through smart community platform to community residents. However, in practice, residents have limited SCS acceptance, which deserves to be further investigated in the literature. This study investigates the SCS adoption of residents by integrating technological belief factors (perceived usefulness and enjoyment), and social influence factor (affective community commitment). A survey of 191 residents identifies perceived usefulness, perceived enjoyment, and affective community commitment as important determinants of SCS adoption. Affective community commitment weakens the effect of perceived enjoyment yet strengthen the effect of perceived usefulness on SCS adoption. Our study fills the research gap on smart community as well as enriches the IT acceptance literature. This study also offers practical recommendations that can aid practitioners in conducting smart community programs
Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion
Accurate 3D reconstruction of the hand and object shape from a hand-object
image is important for understanding human-object interaction as well as human
daily activities. Different from bare hand pose estimation, hand-object
interaction poses a strong constraint on both the hand and its manipulated
object, which suggests that hand configuration may be crucial contextual
information for the object, and vice versa. However, current approaches address
this task by training a two-branch network to reconstruct the hand and object
separately with little communication between the two branches. In this work, we
propose to consider hand and object jointly in feature space and explore the
reciprocity of the two branches. We extensively investigate cross-branch
feature fusion architectures with MLP or LSTM units. Among the investigated
architectures, a variant with LSTM units that enhances object feature with hand
feature shows the best performance gain. Moreover, we employ an auxiliary depth
estimation module to augment the input RGB image with the estimated depth map,
which further improves the reconstruction accuracy. Experiments conducted on
public datasets demonstrate that our approach significantly outperforms
existing approaches in terms of the reconstruction accuracy of objects.Comment: Accepted by IEEE Transactions on Image Processing (TIP
SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum
Failure indexing is a longstanding crux in software testing and debugging,
the goal of which is to automatically divide failures (e.g., failed test cases)
into distinct groups according to the culprit root causes, as such multiple
faults in a faulty program can be handled independently and simultaneously.
This community has long been plagued by two challenges: 1) The effectiveness of
division is still far from promising. Existing techniques only employ a limited
source of run-time data (e.g., code coverage) to be failure proximity, which
typically delivers unsatisfactory results. 2) The outcome can be hardly
comprehensible. A developer who receives the failure indexing result does not
know why all failures should be divided the way they are. This leads to
difficulties for developers to be convinced by the result, which in turn
affects the adoption of the results. To tackle these challenges, in this paper,
we propose SURE, a viSUalized failuRe indExing approach using the program
memory spectrum. We first collect the run-time memory information at preset
breakpoints during the execution of failed test cases, and transform it into
human-friendly images (called program memory spectrum, PMS). Then, any pair of
PMS images that serve as proxies for two failures is fed to a trained Siamese
convolutional neural network, to predict the likelihood of them being triggered
by the same fault. Results demonstrate the effectiveness of SURE: It achieves
101.20% and 41.38% improvements in faults number estimation, as well as 105.20%
and 35.53% improvements in clustering, compared with the state-of-the-art
technique in this field, in simulated and real-world environments,
respectively. Moreover, we carry out a human study to quantitatively evaluate
the comprehensibility of PMS, revealing that this novel type of representation
can help developers better comprehend failure indexing results.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
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