Results 71 to 80 of about 6,545,527 (327)
Contrastive Boundary Learning for Point Cloud Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance.
Liyao Tang +4 more
semanticscholar +1 more source
Cloud Point Extraction in the Determination of Drugs in Biological Matrices.
Journal of Chromatographic Science, 2020 Cloud point extraction (CPE) is a simple, safe and environment-friendly technique used in the preparation of various samples. It was primarily developed for the assessment of environmental samples, especially analyzed for metals. Recently, this technique
Grzegorz Kojro, P. Wroczynski
semanticscholar +1 more source
NoiseTrans: Point Cloud Denoising with Transformers [PDF]
arXiv, 2023 Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream tasks. The paper aims to recover the underlying surface of noisy point clouds. We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising.
arxiv
Best Buddies Registration for Point Clouds [PDF]
, 2021 Accepted to ACCV ...
Drory, Amnon +3 more
openaire +3 more sources
Point Cloud Audio Processing [PDF]
2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021 Most audio processing pipelines involve transformations that act on fixed-dimensional input representations of audio. For example, when using the Short Time Fourier Transform (STFT) the DFT size specifies a fixed dimension for the input representation. As a consequence, most audio machine learning models are designed to process fixed-size vector inputs
Subramani, Krishna, Smaragdis, Paris
openaire +2 more sources
End-to-End Point Cloud Completion Network with Attention Mechanism
Sensors, 2022 We propose a conceptually simple, general framework and end-to-end approach to point cloud completion, entitled PCA-Net. This approach differs from the existing methods in that it does not require a “simple” network, such as multilayer perceptrons (MLPs),
Yaqin Li +4 more
doaj +1 more source
SDFReg: Learning Signed Distance Functions for Point Cloud Registration [PDF]
arXiv, 2023 Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds. To this end, we propose a novel point cloud registration framework for these imperfect point clouds.
arxiv
RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
Applied SciencesThe 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.
Yazhen Han +3 more
doaj +1 more source
Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds
Remote Sensing, 2021 Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity.
Shiming Li +4 more
doaj +1 more source
PointGuard: Provably Robust 3D Point Cloud Classification [PDF]
arXiv, 2021 3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, adding, and/or deleting a small ...
arxiv