Unsupervised point cloud representation learning with deep neural networks: A survey
Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
A Survey of Non‐Rigid 3D Registration
Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …
Scoop: Self-supervised correspondence and optimization-based scene flow
Scene flow estimation is a long-standing problem in computer vision, where the goal is to
find the 3D motion of a scene from its consecutive observations. Recently, there have been …
find the 3D motion of a scene from its consecutive observations. Recently, there have been …
Ogc: Unsupervised 3d object segmentation from rigid dynamics of point clouds
In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike
all existing methods which usually require a large amount of human annotations for full …
all existing methods which usually require a large amount of human annotations for full …
Self-supervised learning for multimodal non-rigid 3d shape matching
The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …
meshes, as well as for shapes represented as point clouds. While point clouds are a …
CorrI2P: Deep image-to-point cloud registration via dense correspondence
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding
3D point cloud is establishing 2D-3D correspondence between them, we propose the first …
3D point cloud is establishing 2D-3D correspondence between them, we propose the first …
3d implicit transporter for temporally consistent keypoint discovery
Keypoint-based representation has proven advantageous in various visual and robotic
tasks. However, the existing 2D and 3D methods for detecting keypoints mainly rely on …
tasks. However, the existing 2D and 3D methods for detecting keypoints mainly rely on …
Diffusion 3d features (diff3f): Decorating untextured shapes with distilled semantic features
NS Dutt, S Muralikrishnan… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
We present Diff3F as a simple robust and class-agnostic feature descriptor that can be
computed for untextured input shapes (meshes or point clouds). Our method distills diffusion …
computed for untextured input shapes (meshes or point clouds). Our method distills diffusion …
Unsupervised 3d pose transfer with cross consistency and dual reconstruction
The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh
while preserving the identity information (eg, face, body shape) of the target mesh. Deep …
while preserving the identity information (eg, face, body shape) of the target mesh. Deep …
Correspondence-free point cloud registration via feature interaction and dual branch [application notes]
Point cloud registration, which effectively coincides the source and target point clouds, is
generally implemented by geometric metrics or feature metrics. In terms of resistance to …
generally implemented by geometric metrics or feature metrics. In terms of resistance to …