Comprehensive review of deep learning-based 3d point cloud completion processing and analysis
Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
Hyperbolic chamfer distance for point cloud completion
Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …
point clouds in point cloud completion, as well as a loss function for (deep) learning …
Proxyformer: Proxy alignment assisted point cloud completion with missing part sensitive transformer
Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …
High fidelity 3d hand shape reconstruction via scalable graph frequency decomposition
Despite the impressive performance obtained by recent single-image hand modeling
techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This …
techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This …
InfoCD: a contrastive chamfer distance loss for point cloud completion
A point cloud is a discrete set of data points sampled from a 3D geometric surface. Chamfer
distance (CD) is a popular metric and training loss to measure the distances between point …
distance (CD) is a popular metric and training loss to measure the distances between point …
Neusdfusion: A spatial-aware generative model for 3d shape completion, reconstruction, and generation
Abstract 3D shape generation aims to produce innovative 3D content adhering to specific
conditions and constraints. Existing methods often decompose 3D shapes into a sequence …
conditions and constraints. Existing methods often decompose 3D shapes into a sequence …
Weakly supervised class-agnostic motion prediction for autonomous driving
Understanding the motion behavior of dynamic environments is vital for autonomous driving,
leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds …
leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds …
Democratising 2d sketch to 3d shape retrieval through pivoting
This paper studies the problem of 2D sketch to 3D shape retrieval, but with a focus on
democratising the process. We would like this democratisation to happen on two fronts:(i) to …
democratising the process. We would like this democratisation to happen on two fronts:(i) to …
Learning geometric transformation for point cloud completion
Point cloud completion aims to estimate the missing shape from a partial point cloud.
Existing encoder-decoder based generative models usually reconstruct the complete point …
Existing encoder-decoder based generative models usually reconstruct the complete point …
Neuralvdb: High-resolution sparse volume representation using hierarchical neural networks
We introduce NeuralVDB, which improves on an existing industry standard for efficient
storage of sparse volumetric data, denoted VDB [Museth], by leveraging recent …
storage of sparse volumetric data, denoted VDB [Museth], by leveraging recent …