1,086 research outputs found
Simulation of the integrated controller of the anti-lock braking system
Author name used in this publication: K. W. E. ChengVersion of RecordPublishe
Free Boundary Minimal Surfaces in the Unit Three-Ball via Desingularization of the Critical Catenoid and the Equatorial Disk
We construct a new family of high genus examples of free boundary minimal
surfaces in the Euclidean unit 3-ball by desingularizing the intersection of a
coaxial pair of a critical catenoid and an equatorial disk. The surfaces are
constructed by singular perturbation methods and have three boundary
components. They are the free boundary analogue of the Costa-Hoffman-Meeks
surfaces and the surfaces constructed by Kapouleas by desingularizing coaxial
catenoids and planes. It is plausible that the minimal surfaces we constructed
here are the same as the ones obtained recently by Ketover using the min-max
method.Comment: 45 pages, 10 figure
Leveraging BEV Representation for 360-degree Visual Place Recognition
This paper investigates the advantages of using Bird's Eye View (BEV)
representation in 360-degree visual place recognition (VPR). We propose a novel
network architecture that utilizes the BEV representation in feature
extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual
cues and spatial awareness. Our method extracts image features using standard
convolutional networks and combines the features according to pre-defined 3D
grid spatial points. To alleviate the mechanical and time misalignments between
cameras, we further introduce deformable attention to learn the compensation.
Upon the BEV feature representation, we then employ the polar transform and the
Discrete Fourier transform for aggregation, which is shown to be
rotation-invariant. In addition, the image and point cloud cues can be easily
stated in the same coordinates, which benefits sensor fusion for place
recognition. The proposed BEV-based method is evaluated in ablation and
comparative studies on two datasets, including on-the-road and off-the-road
scenarios. The experimental results verify the hypothesis that BEV can benefit
VPR by its superior performance compared to baseline methods. To the best of
our knowledge, this is the first trial of employing BEV representation in this
task
Dolfin: Diffusion Layout Transformers without Autoencoder
In this paper, we introduce a novel generative model, Diffusion Layout
Transformers without Autoencoder (Dolfin), which significantly improves the
modeling capability with reduced complexity compared to existing methods.
Dolfin employs a Transformer-based diffusion process to model layout
generation. In addition to an efficient bi-directional (non-causal joint)
sequence representation, we further propose an autoregressive diffusion model
(Dolfin-AR) that is especially adept at capturing rich semantic correlations
for the neighboring objects, such as alignment, size, and overlap. When
evaluated against standard generative layout benchmarks, Dolfin notably
improves performance across various metrics (fid, alignment, overlap, MaxIoU
and DocSim scores), enhancing transparency and interoperability in the process.
Moreover, Dolfin's applications extend beyond layout generation, making it
suitable for modeling geometric structures, such as line segments. Our
experiments present both qualitative and quantitative results to demonstrate
the advantages of Dolfin
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