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Found 14 papers, 5 papers with code
MGDepth: Motion-Guided Cost Volume For Self-Supervised Monocular Depth In Dynamic Scenarios
Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world.
Smooth Non-Stationary Bandits
In many applications of online decision making, the environment is non-stationary and it is therefore crucial to use bandit algorithms that handle changes.
Cost-aware Defense for Parallel Server Systems against Reliability and Security Failures
Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication.
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
This paper presents a novel point cloud representational learning network, called 3D Dual Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions.
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement.
Transformers in 3D Point Clouds: A Survey
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds
The shape of circle is one of fundamental geometric primitives of man-made engineering objects.
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds
In this work, we propose an almost-universal sampler, in our quest for a sampler that can learn to preserve the most useful points for a particular task, yet be inexpensive to adapt to different tasks, models, or datasets.
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer.
MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking
To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods.
VENet: Voting Enhancement Network for 3D Object Detection
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
We demonstrate these by capturing contextual information at patch, object and scene levels.
Empirical validation of network learning with taxi GPS data from Wuhan, China
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows.
Numerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides comprehensive frameworks for simultaneous registration, deformation, and comparison of shapes.
