DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud

Tian, Yifei; Chen, Long; Song, Wei; Sung, Yunsick; Woo, Sangchul

doi:10.3390/rs13010066

Cited by 9 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They evaluated the quality of the performance of state-of-the-art methods based on deep learning and compared the methods with different publicly available datasets. In Tian et al [37], the authors proposed a dynamic graph convolutional broad network (DGCB-Net) for feature extraction and object recognition from point clouds, and their method was tested on several public datasets and one dataset which they collected.…”

Section: Object Recognitionmentioning

confidence: 99%

“…In a similar way as was used in Section 4.5.1, the stacked observation matrix H(k) can be computed as shown in (36), though, in this case, its dimension is 3s × (9 + 4n l + 3n o ). Finally, the covariance matrix of the observation can be built as a bloc diagonal matrix as shown in (37) being, in this case, a 3s × 3s matrix.…”

Section: Object Feature Re-observationmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Mapping for Autonomous Subsea Intervention

Vallicrosa

Himri

Ridao

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

show abstract

Section: Object Recognitionmentioning

confidence: 99%

Section: Object Feature Re-observationmentioning

confidence: 99%

Semantic Mapping for Autonomous Subsea Intervention

Vallicrosa

Himri

Ridao

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…With the easy acquisition of 3D point cloud data and the maturity of methods for processing 3D point cloud data, 3D vision related tasks have received more and more attention. 3D vision has been applied in many fields, such as: object recognition in point clouds, 1,2 point cloud registration, 3,4 shape reconstruction 5,6 , 6D pose estimation. 7,8 These applications may have to process a large number of point clouds and may capture noisy data when collecting data in different scenarios or using different 3D sensors.…”

Section: Introductionmentioning

confidence: 99%

LA-Net: LSTM and attention based point cloud down-sampling and its application

Lin

Liu

Zhang

et al. 2023

Measurement and Control

View full text Add to dashboard Cite

At present, the learning-based down-sampling method has achieved good result by using the loss of subsequent tasks to optimize the process of sampling points selection. However, for the current methods there still has two problems, on the one hand, the sampled points may not be in the original point cloud, and the sampled generated points of the sample need to be matched with the original point cloud (re-matching problem), on the other hand, the number of points obtained after matching may be insufficient for the sampling demand, which need to be supplemented by Farthest Point Sampling, nearest neighbor point, or other methods (re-supplement problem). In order to solve the above problems of re-matching and re-supplement in the learning-based sampling method, we propose a new sampling network which based on high score selection strategy. In our method, LSTM (Long Short Term Memory) is used as the feature extraction method to obtain the relational weight which relative to the subsequent tasks. The attention mechanism is used to select the high score points. The experimental results show that the performances of our network (We dubbed it as LA-Net) are better than other sampling methods and achieve the state-of-the-art in the unified benchmark. Detailed ablation studies have also been carried out, which provably affirm the advantage of each component we proposed. Finally, the methods in this paper are applied to a real backbone network to optimize its performance, and real robot grasping experiments are designed to realize automatic robot grasping of multi-target objects in real scenes.

show abstract

“…Recently, studies on the analysis of graphs using machine learning have been gaining more attention because of the outstanding expressive power of graphs. For instance, graphs can be used to denote a large number of applications across different scopes, including the extraction of topologies and geometries from 3D object detection or point cloud [12]- [14], natural science problems [15], [16], pharmaceutical research [17], [18] and other areas [19]. Graph computation, which is a unique form of data structure for supervised and unsupervised learning strategies, focuses on tasks, such as clustering, link prediction, and classification.…”

Section: Introductionmentioning

confidence: 99%

An efficient activity recognition for homecare robots from multi-modal communication dataset

Yani

Yamada

Siow

et al. 2023

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Human environments are designed and managed by humans for humans. Thus, adding robots to interact with humans and perform specific tasks appropriately is an essential topic in robotics research. In recent decades, object recognition, human skeletal, and face recognition frameworks have been implemented to support the tasks of robots. However, recognition of activities and interactions between humans and surrounding objects is an ongoing and more challenging problem. Therefore, this study proposed a graph neural network (GNN) approach to directly recognize human activity at home using vision and speech teaching data. Focus was given to the problem of classifying three activities, namely, eating, working, and reading, where these activities were conducted in the same environment. From the experiments, observations, and analyses, this proved to be quite a challenging problem to solve using only traditional convolutional neural networks (CNN) and video datasets. In the proposed method, an activity classification was learned from a 3D detected object corresponding to the human position. Next, human utterances were used to label the activity from the collected human and object 3D positions. The experiment, involving data collection and learning, was demonstrated by using human-robot communication. It was shown that the proposed method had the shortest training time of 100.346 seconds with 6000 positions from the dataset and was able to recognize the three activities more accurately than the deep layer aggregation (DLA) and X3D networks with video datasets.

show abstract

DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud

Cited by 9 publications

References 33 publications

Semantic Mapping for Autonomous Subsea Intervention

Semantic Mapping for Autonomous Subsea Intervention

LA-Net: LSTM and attention based point cloud down-sampling and its application

An efficient activity recognition for homecare robots from multi-modal communication dataset

Contact Info

Product

Resources

About