Panoptic-PolarNet: A Framework for Point Cloud Segmentation with LiDAR
Panoptic-PolarNet is a framework developed for point cloud segmentation using LiDAR technology. This framework is particularly relevant to applications in urban street scenes where instances are severely occluded. Panoptic-PolarNet overcomes this issue by learning both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation. This results in
What is Point-GNN?
Point-GNN, or Point-based Graph Neural Network, is a technology that can detect objects in a LiDAR point cloud. It uses algorithms to predict the shape and category of objects based on vertices in the graph.
How Does Point-GNN Work?
LiDAR point clouds are created by shooting laser beams at objects and measuring the time it takes for the beams to come back. By using this data, Point-GNN can identify objects and their shapes. The network uses graph convolutional operators to
PointASNL: A Revolutionary Neural Network for Point Cloud Processing
In recent years, the field of computer vision has seen exciting advancements in 3D object recognition and reconstruction with the advent of deep learning algorithms. One particularly promising area of research is point cloud processing, which involves analyzing the 3D coordinates of individual points in an object or scene. However, one major challenge of analyzing point clouds is the sheer amount of data involved - even a simp
Overview of PQ-Transformer
PQ-Transformer, also known as PointQuad-Transformer, is an architecture used to predict 3D objects and layouts from point cloud input. Unlike existing methods that estimate layout keypoints or edges, PQ-Transformer directly parameterizes room layouts as a set of quads. Additionally, it employs a physical constraint loss function that discourages object-layout interference.
Point Cloud Feature Learning Backbone
In the PQ-Transformer architecture, given an input 3D p
Overview of PREDATOR
PREDATOR is a cutting-edge model for pairwise point-cloud registration with deep attention to the overlap region. Point-cloud registration is the process of aligning two point clouds in order to find the transformation that maps one to the other. It is used in various applications such as robotics, augmented reality, and self-driving cars.
What is Point-Cloud Registration?
Point clouds are sets of 3D points that represent the shape of an object or a scene. Point-cloud re
Understanding RPM-Net: A Robust Point Matching Technique
If you are familiar with computer science, you might have heard of the term RPM-Net. It refers to an end-to-end differentiable deep network that works for robust point matching using learned features. The network deals with the issue of noisy and outlier points, making it a desired method for point matching. To understand what this technology is all about, we need to break it down into its components.
The Basics of Point Matching
Befor
Overview of Voxel R-CNN
Voxel R-CNN is an advanced technique used for 3D object detection. It is a two-stage process consisting of a 3D backbone network, a 2D bird-eye-view Region Proposal Network, and a detect head.
Process of Voxel R-CNN
The Voxel R-CNN process involves breaking down point clouds into regular voxels, which are then fed into the 3D backbone network for feature extraction. Once features are extracted from 3D volumes, they are converted into bird-eye-view representations. The
The YOHO framework for point cloud registration
If you work with 3D data, you know how important it is to be able to align different point clouds in a reliable, repeatable way. Point cloud registration is the process of finding the spatial transformation that brings two point clouds into a common reference frame, meaning that corresponding points from the two clouds can be matched up.
Researchers have proposed many algorithms for point cloud registration, but they often suffer from sensitivity