Results 11 to 20 of about 5,571,343 (339)

AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds.
M. Zhou, Z. Kang, Z. Wang, M. Kong
doaj   +3 more sources

PCT: Point cloud transformer [PDF]

open access: yesComputational Visual Media, 2020
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning.
Meng-Hao Guo   +5 more
semanticscholar   +7 more sources

Adaptive Clustering for Point Cloud

open access: yesSensors
The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to ...
Zitao Lin   +6 more
doaj   +3 more sources

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests [PDF]

open access: yesRemote Sensing, 2017
This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF ...
Huan Ni, Xiangguo Lin, Jixian Zhang
doaj   +3 more sources

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a
Xumin Yu   +5 more
semanticscholar   +1 more source

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models [PDF]

open access: yesNeural Information Processing Systems, 2023
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a novel framework that harnesses VFMs for segmenting diverse automotive point cloud ...
You-Chen Liu   +7 more
semanticscholar   +1 more source

PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction [PDF]

open access: yesComputer Vision and Pattern Recognition, 2023
Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision. In this paper, we propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising ...
Luke Melas-Kyriazi   +2 more
semanticscholar   +1 more source

Diffusion Probabilistic Models for 3D Point Cloud Generation [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
Shitong Luo, Wei Hu
semanticscholar   +1 more source

Rotation-Invariant Transformer for Point Cloud Matching [PDF]

open access: yesComputer Vision and Pattern Recognition, 2023
The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number
Hao Yu   +6 more
semanticscholar   +1 more source

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [PDF]

open access: yes2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Yin Zhou, Oncel Tuzel
semanticscholar   +1 more source

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