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Rotation-invariant transformer for point cloud matching
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 …
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …
Discriminative correspondence estimation for unsupervised rgb-d point cloud registration
Point cloud registration is a fundamental task for estimating the rigid transformation matrix
between two point clouds, and is regarded as a prerequisite for downstream vision tasks …
between two point clouds, and is regarded as a prerequisite for downstream vision tasks …
Egosg: Learning 3d scene graphs from egocentric rgb-d sequences
Constructing a 3D scene graph of an environment is essential for agents and smart glasses
assistants to develop an understanding of their surroundings and predict relationships …
assistants to develop an understanding of their surroundings and predict relationships …
HECPG: hyperbolic embedding and confident patch-guided network for point cloud matching
As a fundamental problem in photogrammetry and remote sensing, terrestrial laser scanner
point cloud matching aims to seek a correspondence set that can match two partially …
point cloud matching aims to seek a correspondence set that can match two partially …
Pcr-cg: Point cloud registration via deep explicit color and geometry
In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly
embedding the color signals into geometry representation. Different from the previous SOTA …
embedding the color signals into geometry representation. Different from the previous SOTA …
Deep learning-based low overlap point cloud registration for complex scenario: The review
Most studies on point cloud registration have established the problem in the case of ideal
point cloud data. Although the state-of-the-art approaches have achieved amazing results …
point cloud data. Although the state-of-the-art approaches have achieved amazing results …
Non-rigid shape registration via deep functional maps prior
In this paper, we propose a learning-based framework for non-rigid shape registra-tion
without correspondence supervision. Traditional shape registration techniques typically rely …
without correspondence supervision. Traditional shape registration techniques typically rely …
Spherenet: Learning a noise-robust and general descriptor for point cloud registration
Point cloud registration aims to estimate a transformation that aligns point clouds collected
from different perspectives. In learning-based point cloud registration, a robust descriptor is …
from different perspectives. In learning-based point cloud registration, a robust descriptor is …
Bilevel fusion with local and global cues for point cloud upsampling
This study focuses on point cloud upsampling, crucial in 3-D data processing but hindered
by current 3-D sensor limitations. Point clouds from RGB-D cameras and light detection and …
by current 3-D sensor limitations. Point clouds from RGB-D cameras and light detection and …
Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem
Establishing reliable correspondences is essential for 3D and 2D-3D registration tasks.
Existing methods commonly leverage geometric or semantic point features to generate …
Existing methods commonly leverage geometric or semantic point features to generate …