ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Nagy, Balázs; Kovács, Lóránt; Benedek, Csaba

doi:10.1109/lra.2021.3105721

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…Creating datasets for CD is more complicated than other tasks; it requires traversing point clouds not just at one epoch, but at two or more epochs. Given the lack of annotated data, to properly train the models, researchers use a variety of strategies, including transfer learning [156], data augmentation [157,158], and Point Clouds Generative Adversarial Networks (PC-GAN) [159,160]. Although these techniques alleviate some of the problems, coupled with a lack of samples, further improvements are still needed.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Three Dimensional Change Detection Using Point Clouds: A Review

et al. 2022

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Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Three Dimensional Change Detection Using Point Clouds: A Review

et al. 2022

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“…The human pose estimation task can be applied in surveillance applications, which demand real-time solutions. To address this need, our approach involves transforming the representation of the NRCS lidar point cloud from 3D Cartesian coordinates to a spherical polar coordinate system, similar to our previous works in [ 39 , 40 ]. We generate a 2D pixel grid by discretizing the horizontal and vertical FoVs, where each 3D point’s distance from the sensor is mapped to a pixel determined by corresponding azimuth and elevation values.…”

Section: Proposed Methodsmentioning

confidence: 99%

LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern

Kovács,

Bódis,

Benedek

2024

Sensors

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In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth.

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“…Range images are widely used, compact representations of Lidar-based depth measurements [1], [3], [16], which enable to adopt 2D convolution operations and effective image-based neural network architectures [4], [6] during processing.…”

Section: A Range Image Generationmentioning

confidence: 99%

“…For dynamic environment perception and recognition tasks such as advanced scene analysis and understanding, repeti-tive, typically rotating multi-beam (RMB) Lidar sensors (e.g., Ouster OS1 or Velodyne Puck models) [1] are commonly utilized devices. RMB Lidars can produce real-time point cloud streams (300k-2M points/s), however, their measurements have low spatial density, and their field of view (FoV) coverage is constant through the whole scanning process: Their vertical resolution is fixed by the number of the laser beams , while their horizontal resolution depends on the sensor's rotation frequency (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

ST-DepthNet: A Spatio-Temporal Deep Network for Depth Completion Using a Single Non-Repetitive Circular Scanning Lidar

Zováthi

Palffy

Jankó

et al. 2023

IEEE Robot. Autom. Lett.

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In this paper, we propose a novel depth image completion technique based on sparse consecutive measurements of a non-repetitive circular scanning (NRCS) Lidar, demonstrating the capabilities of a new, compact, and accessible sensor technology for dense range mapping of highly dynamic scenes. Our deep network called ST-DepthNet is composed of a spatio-temporally (ST) extended U-Net architecture, which accepts a very sparse range data sequence as input and produces a dense depth image stream of the same field-of-view ensuring a high level of spatial details and accuracy. For evaluation, we have constructed a new urban dataset, that -to our best knowledge as the first open Benchmark in this field -comprises various simulated and realworld NRCS Lidar data samples, allowing us to simultaneously train our model on synthetic data with Ground Truth, and to validate the result via real NRCS Lidar measurements. Using this new dataset, we have shown the superiority of our method against a densified depth map obtained from the raw sensor stream, and against two independent state-of-the-art deep-learning based Lidar-only depth completion methods.

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ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Cited by 14 publications

References 21 publications

Three Dimensional Change Detection Using Point Clouds: A Review

Three Dimensional Change Detection Using Point Clouds: A Review

LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern

ST-DepthNet: A Spatio-Temporal Deep Network for Depth Completion Using a Single Non-Repetitive Circular Scanning Lidar

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