Research Interests
GPU Accelerators
Kernel designs for high-performance computing that leverage GPUs for large-scale data processing.
Hardware-Aware 3D Vision
Accelerating complex computer vision algorithms using FPGAs and GPUs for real-time applications.
Large-Scale Data Processing
Efficient algorithms and systems for large-scale graph and data processing on modern hardware.
About Me
I am an assistant professor at the Institute of Science Tokyo (formerly Tokyo Institute of Technology), School of Computing. I received my Ph.D. in Intelligent Systems Engineering from the University of Tsukuba. Before joining the Institute of Science Tokyo, I was a researcher at the National Institute of Advanced Industrial Science and Technology (AIST).
News
Feb 2026
Paper
"Memory Efficient Point Cloud Registration Accelerator on FPGA" accepted to
ICRA'26
Sep 2025
Paper
"Fast Approximate Aggregation with Error Guarantee Using Encoded Bit-slice Indexing" accepted to
iiWAS'25
Jun 2025
Paper
"3D GNLM: Efficient 3D Non-Local Means Kernel with Nested Reuse Strategies for Embedded GPUs" accepted to
TACO'25
May 2025
Paper
"FSAC-IA: A Hierarchical Constructed SAC-IA Algorithm for Point Cloud Alignment Acceleration" accepted to
ICIP'25
May 2025
Paper
"Unified Schema-Driven Graph Polystore: Achieving Transparency in Multi-Model Integration and Migration" accepted to
DEXA'25
Mar 2025
Paper
"Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs" accepted to
TECS'25
Jan 2025
Paper
"Efficient Parallel Implementation of Non-Local Means Algorithm on GPU" accepted to
GPGPU'25
Jan 2025
Paper
"Accelerating Nearest Neighbor Search in 3D Point Cloud Registration on GPUs" accepted to
TACO'25
Selected Research
TACO 2025
Accelerating Nearest Neighbor Search in 3D Point Cloud Registration on GPUs
Qiong Chang, Weimin Wang, Jun Miyazaki
A GPU-accelerated method to significantly speed up nearest neighbor search for 3D point cloud registration, enhancing real-time performance in high-density spatial data processing.
GPU
3D Vision
Point Cloud
TECS 2025
Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs
Qiong Chang, Xinyuan Chen, Weimin Wang, Xiang Li, Jun Miyazaki
Two methods to accelerate the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection.
Embedded GPU
Feature Detection
TACO 2025
3D GNLM: Efficient 3D Non-Local Means Kernel with Nested Reuse Strategies for Embedded GPUs
Xiang Li, Qiong Chang*, Yun Li, Jun Miyazaki
An efficient parallel implementation of the 3D Non-Local Means denoising algorithm on GPU, significantly accelerating performance for high-resolution medical image processing tasks.
GPU
Medical Imaging
TACO 2024
An Optimized GPU Implementation for GIST Descriptor
Xiang Li, Qiong Chang*, Aolong Zha, Shijie Chang, Yun Li, Jun Miyazaki
An optimized GPU-based implementation of the GIST descriptor, significantly accelerating image feature extraction for large-scale visual processing tasks.
GPU
Feature Extraction
JPDC 2023
Multi-Directional Sobel Operator Kernel on GPUs
Qiong Chang, Xiang Li, Yun Li, Jun Miyazaki
A GPU-accelerated multi-directional Sobel operator kernel for efficient and parallel edge detection across multiple gradient orientations.
GPU
Edge Detection
IEEE TSMC 2024
TinyStereo: A Tiny Coarse-to-Fine Framework for Vision-based Depth Estimation on Embedded GPUs
Qiong Chang, Xin Xu, Aolong Zha, Yongqing Sun, Yun Li
A lightweight coarse-to-fine stereo matching framework optimized for embedded GPUs, enabling efficient and accurate depth estimation under constrained resources.
Embedded GPU
Stereo Matching
Depth Estimation
JSA 2022
Efficient Stereo Matching on Embedded GPUs with Zero-Means Cross Correlation
Qiong Chang, Aolong Zha, Weimin Wang, Xin Liu, Masaki Onishi, Lei Lei, Tsutomu Maruyama
Fast ZNCC feature matching on embedded GPUs, offering an effective real-time alternative to traditional Census in stereo matching.
Embedded GPU
Stereo Matching
