India is the world's largest producer of pomegranates, but plant diseases significantly impact crop productivity, leading to major agricultural losses annually. Fusarium wilt, caused by Fusarium oxysporum f. sp. c...
详细信息
In the abstract, The effective identification and categorization of mould material faults are critical for ensuring the quality and dependability of substances in a variety of industrial settings. This paper, it provi...
详细信息
The paper solves the problem of discovering the dependence of mechanical properties of a metal casting on the chemical composition of source materials by constructing a system of models for an object with many input a...
详细信息
This article introduces the monitoring method of ship navigation under the situation of existing mission. Mission process monitoring and monitoring tips feedback can be achived through data structuring、current missio...
详细信息
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize geometric structure features in downsampled points (patches) to seek correspondences, then propagate ...
详细信息
ISBN:
(纸本)9781665491907
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize geometric structure features in downsampled points (patches) to seek correspondences, then propagate these sparse patch correspondences to the dense level in the corresponding patches' neighborhood. However, they neglect the explicit global scale rigid constraint at the dense level point matching. We claim that the explicit isometry-preserving constraint in the dense level on a global scale is also important for improving feature representation in the training stage. To this end, we propose a Graph Matching Optimization based Network (GMONet for short), which utilizes the graph-matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation. Specifically, we exploit a partial graph-matching optimizer to enhance the super point (i.e., down-sampled key points) features and a full graph-matching optimizer to improve the dense level point features in the overlap region. Meanwhile, we leverage the inexact proximal point method and the mini-batch sampling technique to accelerate these two graph-matching optimizers. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch and the KITTI datasets. The experimental results show that our method performs competitively compared to state-of-the-art baselines.
The report seeks to develop an algorithm for polling measurement modules and estimating the error of indirect measurements in a personalized distributed information and measurement system with autonomous measurement m...
详细信息
This paper mainly analyzes the application of laser Time-of-flight (ToF) measurement in the measurement of medium-range distance of non-cooperative targets in space, in order to handle the problem of different measure...
详细信息
The paper proposes an approach to modelling the processes of smart industrial additive manufacturing and related types of attacks in laboratory conditions. The objective of such modelling is to analyze potentially rel...
详细信息
This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definiti...
详细信息
A long running data-intensive computational application acquires costly computing resources. With the emerging new architectures, like computing systems with multiple nodes of many-core CPUs and accelerators, while do...
详细信息
暂无评论