IEEE

Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions

Abstract: Multi-view 3D reconstruction generally adopts the feature fusion strategy to guide the generation of 3D shape for objects with different views. Empirically, the correspondence learning of ...
IEEE

MOJO: MOtion Pattern Learning and JOint-Based Fine-Grained Mining for Person Re-Identification Based on 4D LiDAR Point Clouds

Abstract: Person Re-identification (ReID) primarily involves the extraction of discriminative representations derived from morphological characteristics, gait patterns, and related attributes. While ...
IEEE

PointWavelet: Learning in Spectral Domain for 3-D Point Cloud Analysis

Abstract: With recent success of deep learning in 2-D visual recognition, deep-learning-based 3-D point cloud analysis has received increasing attention from the community, especially due to the rapid ...
IEEE

A Content-Adaptive Hierarchical Deep Learning Model for Detecting Arbitrary-Oriented Road Surface Elements Using MLS Point Clouds

Abstract: Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information ...
IEEE

Revolutionizing Point Cloud Classification with Multiview Self-Supervised Learning

Abstract: Self-supervised models are shaping the future of point cloud processing by minimizing reliance on labeled data and addressing the challenges associated with point cloud annotation.
IEEE

Class Incremental Learning With Less Forgetting Direction and Equilibrium Point

Abstract: Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate this ...
IEEE

DSANet: Dynamic and Structure-Aware GCN for Sparse and Incomplete Point Cloud Learning

Abstract: Learning 3-D structures from incomplete point clouds with extreme sparsity and random distributions is a challenge since it is difficult to infer topological connectivity and structural ...
IEEE

DNRHP: Temporal Network Representation Learning via Hawkes Point Process

Abstract: Graph neural networks (GNNs) have significantly advanced our ability to mine structured data, playing a central role in areas such as social networks and recommendation systems. However, ...
Hosted on MSN

New Mexico State University offering tours of Apache Point Observatory

More for You The Commander in Chief Is Not Okay Federal employee taken into custody following "active shooter hoax" at NJ's largest military base China reacts to Donald Trump's new tariffs Chaos at ...
IEEE

Learning General and Distinctive 3D Local Deep Descriptors for Point Cloud Registration

Abstract: An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different ...
thenewsmarket

NMSU’s Apache Point Observatory to host open house October 11

WHAT: Apache Point Observatory (APO), in partnership with New Mexico State University’s Department of Astronomy, will host an open house from 10 a.m. to 2 p.m. Saturday, October 11. This event is free ...
IEEE

Deep Learning Deployment on Big Data Infrastructure Using Apache Spark (Case Study: COVID-19 Detection Using X-Ray Images)

Abstract: It is possible to use GPU (Graphic Processing Unit) to increase deep learning performance. This requires us to invest in separate GPUs, which can be relatively expensive. However, if we ...