Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and select...
Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakl...
It has become routine to directly process point clouds using a combination of shared multilayer perceptrons and aggregate functions. However, this practice has difficulty capturing the local information of point clouds, leading to information loss. N...
Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-triv...
With the rapid growth of real-world untrimmed videos, video moment localization (VML) has become a fundamental task in query-guided video understanding, aiming to identify the temporal moment that semantically matches a natural language query. Althou...
We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the...
Three-dimensional object detection from LiDAR point clouds is essential for autonomous driving, yet existing methods typically rely on costly and extensively annotated datasets. Self-supervised masked autoencoders (MAE) provide a promising alternativ...
The prediction of charging point occupancy enables electric vehicle users to better plan their charging processes and thus promotes the acceptance of electromobility. The study uses Adaptive Charging Network data to investigate a public and a workpla...
Segmentation of 3D point clouds is essential for applications such as environmental monitoring and autonomous navigation, where making accurate distinctions between different classes from high-resolution 3D datasets is critical. Segmenting 3D point c...
The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learnin...
Infrared small-target detection (IRSTD) has benefited from deep learning, yet most existing methods still rely on dense pixel-level annotations, which are costly and often unreliable for tiny and weak targets. Point supervision offers a more practica...
The semantic segmentation of point clouds has significant applications in fields such as autonomous driving, robot vision, and smart cities. As LiDAR technology continues to develop, point clouds have gradually become the main type of 3D data. Howeve...
Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform po...
Point cloud semantic segmentation is a fundamental task for 3D scene understanding. However, acquiring dense point-wise annotations is labor-intensive and expensive. To alleviate the annotation burden, weakly supervised methods using extremely sparse...
Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their stru...
In the development of digital twin (DT) workshops, constructing accurate DT models has become a key step toward enabling intelligent manufacturing. To address challenges such as incomplete data acquisition, noise sensitivity, and the heavy reliance o...
Urban airborne laser scanning (ALS) point clouds cover extensive geographical areas, rendering dense point-level annotation economically prohibitive and limiting the feasibility of fully supervised learning. In weakly supervised settings for urban AL...
With the proliferation of large-scale 3D point cloud datasets, the high cost of per-point annotation has spurred the development of weakly supervised semantic segmentation methods. Current popular research mainly focuses on single-scale classificatio...
For networks that process 3D data, estimating the orientation and position of 3D objects is a challenging task. This is because the traditional networks are not robust to the rotation of the data, and their internal workings are largely opaque and un...
Accurate as-built models of offshore platforms require component-level information from dense laser scans, but point-wise annotation is costly. We present Offshore Semi Supervised Contrastive Segmentation (OSCS), a framework for drilling platform poi...
Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robot...
Indoor 3D point cloud semantic segmentation is a fundamental task for fine-grained scene understanding and intelligent perception. Due to the prohibitive cost of dense point-wise annotations, weakly supervised learning has emerged as a promising alte...
Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method...
Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervise...
Adversarial manipulation of perception is usually scored per frame by an adversarial success rate (ASR). For a remotely supervised drone this is insufficient: a manipulation is dangerous when neither the modeled remote-human path nor the onboard auto...
Accurate nuclei instance segmentation requires pixel-level boundary annotation, which is expensive and time-consuming in histopathology images. We propose a sparse point-to-pseudo-instance supervision framework that learns from limited center-point a...
Due to the imbalance between urban and rural development and improper management, the spatial forms of many heritage villages have suffered severe damage, and their landscape styles are gradually being blurred, posing serious challenges to the protec...
Counting small, densely clustered objects from low-altitude aerial views is challenging due to large scale variations, complex backgrounds, and severe occlusion, which often degrade the performance of fully supervised or density-regression methods. T...
Point cloud scene flow estimation aims to capture the three-dimensional motion of each point in a sequence of point clouds. Although progress has occurred in this field, existing methods often face significant challenges. In particular, two key issue...
Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especial...
With the application of the random sampling method in the down-sampling of point clouds data, the processing speed of point clouds has been greatly improved. However, the utilization of semantic information is still insufficient. To address this prob...
Rotated Bounding Boxes (RBBs) for oriented object detection are labor-intensive and time-consuming to annotate. Single-point supervision offers a cost-effective alternative but suffers from insufficient size and orientation information, leading exist...
Accurate assessment of above-ground biomass (AGB) is essential for optimizing crop growth and enhancing agricultural efficiency. However, predicting above-ground biomass (AGB) presents significant challenges. Traditional point cloud networks often st...
As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction an...
The point clouds obtained directly from three-dimensional scanning devices are often sparse and noisy. Therefore, point cloud upsampling plays an increasingly crucial role in fields such as point cloud reconstruction and rendering. However, point clo...
Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high intere...
Extracting dominant points from complex building outlines is an important task in cartography and building footprint simplification. This study proposes a semi-supervised, geometric-aware graph-Transformer framework for automatic dominant point selec...
In view of the poor performance of traditional feature point detection methods in low-texture situations, we design a new self-supervised feature extraction network that can be applied to the visual odometer (VO) front-end feature extraction module b...
3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation,...
Point cloud upsampling is a fundamental task in 3D vision, yet most existing methods adopt a global and uniform strategy, which is computationally inefficient and fails to address the need for region-specific refinement. To address this challenge, we...
We propose a fully automatic annotation scheme that takes a raw 3D point cloud with a set of fitted CAD models as input and outputs convincing point-wise labels that can be used as cheap training data for point cloud segmentation. Compared with manua...
Most existing point cloud instance segmentation methods require accurate and dense point-level annotations, which are extremely laborious to collect. While incomplete and inexact supervision has been exploited to reduce labeling efforts, inaccurate s...
Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Dense...
The timely detection of fishing activities supports the protection of water resources and aids law enforcement in fishing ban and shoreline inspection scenarios. Fishing rods are highly discriminative objects for identifying such activities; however,...
Accurate measurement of the geometric parameters of trees is a vital part of forest inventory in forestry management. Aerial and terrestrial Light Detection and Ranging (LiDAR) sensors are currently used in forest inventory as an effective and effici...
Real-world point clouds often contain spurious observations that obscure the underlying geometry. Modern point-cloud models struggle to distinguish such outliers from sparse but valid local geometry. We propose a two-stage transformer framework for a...
In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as...
Point cloud semantic segmentation remains challenging under extremely low annotation budgets due to inefficient utilization of sparse labels and sensitivity to data augmentation noise. To address this, we propose a dual-branch consistency learning (D...
Existing point cloud semantic segmentation approaches do not perform well on details, especially for the boundary regions. However, supervised-learning-based methods depend on costly artificial annotations for performance improvement. In this paper,...
In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to det...
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