Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Perceptual Constancy

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analysis of expression and prognostic significance of vimentin and the response to temozolomide in glioma patients.

Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine·2016

Same author

Missing Messages of Clinical Pharmacologic Survey on Inotropic Drug Use in Neonatal Critical Care.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·2016

Same author

Activin pathway enhances colorectal cancer stem cell self-renew and tumor progression.

Biochemical and biophysical research communications·2016

Same author

Clinical outcome of endoscopic covered metal stenting for resolution of benign biliary stricture: Systematic review and meta-analysis.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society·2016

Same author

MicroRNA 182 inhibits CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>+</sup> Treg differentiation in experimental autoimmune encephalomyelitis.

Clinical immunology (Orlando, Fla.)·2016

Same author

Lymphoma and inflammation in the orbit: Diagnostic performance with diffusion-weighted imaging and dynamic contrast-enhanced MRI.

Journal of magnetic resonance imaging : JMRI·2016

Search research articles

Related Experiment Video

Updated: May 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

PillarFocusNet for 3D object detection with perceptual diffusion and key feature understanding.

Yuhan Gao¹, Peng Wang^2,3, Xiaoyan Li¹

¹School of Electronics Information Engineering, Xi'an Technological University, Xi'an, 710021, China.

Scientific Reports

|March 14, 2025

Summary

PillarFocusNet enhances 3D point cloud object detection by optimizing the PointPillars framework using novel sampling and feature extraction methods. This improves bounding box, bird

Keywords:

3D point cloud Attention mechanism Deep learning Feature extraction Object detection

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Related Experiment Videos

Last Updated: May 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

3D point cloud object detection is crucial for autonomous systems.
Existing methods like PointPillars face challenges with sparse and uneven data distribution.
Effective feature extraction and representation are key to improving detection accuracy.

Purpose of the Study:

To introduce PillarFocusNet, an optimized network for 3D point cloud object detection.
To enhance the performance of the PointPillars framework.
To address limitations in handling sparse 3D point cloud data and improve feature representation.

Main Methods:

Pillar Clustering Sampling Method to manage sparse and uneven point cloud data.
Mixed Pooling Dilated Convolution (MPDC) layer for advanced feature extraction.
Space-Channel Synergistic Enhancement Module (SCS-EM) for improved spatial and channel feature representation.

Main Results:

PillarFocusNet demonstrated significant improvements over the baseline PointPillars framework on the KITTI dataset.
Enhancements observed in bbox (1.3%), bev (2.9%), and 3D (3.4%) detection performance.
The proposed methods effectively address data distribution challenges and enhance feature learning.

Conclusions:

PillarFocusNet represents a significant advancement in 3D point cloud object detection.
The novel components effectively improve detection accuracy and robustness.
The publicly available code and models facilitate further research and application.