Results 51 to 60 of about 12,539 (191)

Stereo R-CNN based 3D Object Detection for Autonomous Driving

, 2019
We propose a 3D object detection method for autonomous driving by fully exploiting the sparse and dense, semantic and geometry information in stereo imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect
Chen, Xiaozhi, Li, Peiliang, Shen, Shaojie   +2 more
core   +1 more source

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

Sensors, 2018
Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal ...
Liang Lu, Lin Qi, Yisong Luo, Hengchao Jiao, Junyu Dong   +4 more
doaj   +1 more source

Photometric Stereo in a Scattering Medium [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biological tissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scattered back from the medium (backscatter). Here we make three contributions to address the key modes of
Zak Murez, Tali Treibitz, Ravi Ramamoorthi, David J. Kriegman   +3 more
openaire   +2 more sources

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley   +1 more source

Photometric Stereo Super Resolution via Complex Surface Structure Estimation

IEEE Access
Photometric stereo, which derives per-pixel surface normals from shading cues, faces challenges in capturing high-resolution (HR) images in linear response systems.
Han-Nyoung Lee, Hak Gu Kim
doaj   +1 more source

Photometric Depth Super-Resolution

, 2019
This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational
Cremers, Daniel, Haefner, Bjoern, Peng, Songyou, Quéau, Yvain, Verma, Alok   +4 more
core   +3 more sources

TacScope: A Miniaturized Vision‐Based Tactile Sensor for Surgical Applications

Advanced Robotics Research, EarlyView.
TacScope is a compact, vision‐based tactile sensor designed for robot‐assisted surgery. By leveraging a curved elastomer surface with pressure‐sensitive particle redistribution, it captures high‐resolution 3D tactile feedback. TacScope enables accurate tumor detection and shape classification beneath soft tissue phantoms, offering a scalable, low‐cost ...
Md Rakibul Islam Prince, Sheeraz Athar, Pokuang Zhou, Yu She   +3 more
wiley   +1 more source

Photometric Variability of the mCP Star CS Vir: Evolution of the Rotation Period

, 2017
The aim of this study is to accurately calculate the rotational period of CS\,Vir by using {\sl STEREO} observations and investigate a possible period variation of the star with the help of all accessible data.
Ozuyar, Dogus, Sener, H. Tugca, Stevens, Ian R.   +2 more
core   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Demultiplexing Colored Images for Multispectral Photometric Stereo via Deep Neural Networks

IEEE Access, 2018
Recovering fine-scale surface shapes is a challenging task in computer vision. Multispectral photometric stereo is one of the popular methods as it can handle non-rigid/moving objects and produces per-pixel dense results.
Yakun Ju, Lin Qi, Huiyu Zhou, Junyu Dong, Liang Lu   +4 more
doaj   +1 more source