Anique Akhtar      

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Anique Akhtar
Qualcomm Inc.
Ph.D. Doctorate in Computer and Electrical Engineering

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I am a machine learning researcher specializing in deep learning, data compression, computer vision, and generative modeling. Currently, I work as a Senior Engineer at Qualcomm Inc. on the Multimedia Research and Development (MMRND) team in San Diego, California, where I focus on advancing cutting-edge multimedia technologies.

I earned my Ph.D. in Computer and Electrical Engineering from the University of Missouri–Kansas City, conducting research under Dr. Zhu Li in the Multimedia Computing & Communication Lab. My doctoral work centered on innovative approaches to multimedia systems and machine learning.

Before that, I completed my M.S. in Electrical Engineering at Koç University, Istanbul, Turkey, where I worked with Dr. Sinem Coleri Ergen in the Wireless Networks Laboratory on 60 GHz directional wireless Communication.

My academic journey began with a B.Sc. in Electrical Engineering from Lahore University of Management Sciences (LUMS) in Lahore, Pakistan, laying the foundation for my passion for advanced communication systems and AI-driven technologies.

Announcements

  • Inventor in ten patents. 2025

  • ICIP publication: “J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking”. 2025

  • Inventor in four patents. 2024

  • Publication in Transactions on Image Processing (TIP). “Inter-Frame Compression for Dynamic Point Cloud Geometry Coding”. 2024

  • ICIP publication: “ResNeRF-PCAC: Super Resolving Residual Learning NeRF for High Efficiency Point Cloud Attributes Coding”. 2024

  • MMSP publication: “Sparse Convolution Based Point Cloud Attributes Deblocking with Graph Fourier Latent Representation”. 2024

  • Qualcomm recognition as an IP contributor. 2023

  • Recipient of the annual award for Outstanding Doctoral Student in Electrical and Computer Engineering for the academic year 2021-22. May 2022

  • PU-Dense work accepted in Transactions on Image Processing (TIP). March 2022

Research

  • Gaussian Splatting.

  • Video-based dynamic mesh coding (V-DMC).

  • MPEG WG7 3DGCH: GSC, V-DMC, AI-PCC, V-PCC, G-PCC.

  • Inter-frame and Intra-frame Point Cloud Prediction Schemes.

  • End-to-end Point Cloud Compression.

  • Point Cloud Upsampling.

  • 3D Point Cloud Semantic Segmentation.

  • 3D Point Cloud Denoising and Outlier Removal.

  • Deep Learning-based Point Cloud Processing.

  • Neural Networks and Deep Learning.

  • Wireless Networks (LTE and 5G).

  • OFDM and Waveform design.

  • 3GPP RAN.

  • MAC Protocols for Wireless Communication.

  • mmWave Directional Communication.

Relevant Publications

  • M. Talha, Q. Yang, Z. Li, A. Akhtar, G. Van Der Auwera. “J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking”.
    IEEE International Conference on Image Processing (ICIP), 2025.

  • A. Akhtar, Z. Li, and G. Van der Auwera, “Inter-Frame Compression for Dynamic Point Cloud Geometry Coding”
    IEEE Transactions on Image Processing (TIP). 2024. (arxiv) (link)

  • S. Umair, B. Kathariya, Z. Li, A. Akhtar, G. Van der Auwera. “ResNeRF-PCAC: Super Resolving Residual Learning NeRF for High Efficiency Point Cloud Attributes Coding”.
    IEEE International Conference on Image Processing (ICIP), 2024. (link)

  • M. Talha, B. Kathariya, Z Li, A. Akhtar, G. Van der Auwera. “Sparse Convolution Based Point Cloud Attributes Deblocking with Graph Fourier Latent Representation”.
    International Workshop on Multimedia Signal Processing (MMSP), 2024. (link)

  • A. Akhtar, Z. Li, G. Van der Auwera, L. Li, and J. Chen, “PU-Dense: Sparse Tensor-based Point Cloud Geometry Upsampling” (Project Page) (GitHub)
    IEEE Transactions on Image Processing (TIP). 2022 (link)

  • A. Akhtar, Z. Li, G. Van der Auwera, and J. Chen, “Dynamic Point Cloud Interpolation”
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2022. (link)

  • A. Akhtar, W. Gao, L. Li, Z. Li, W. Jia, and S. Liu, “Video-based Point Cloud Compression Artifact Removal”,
    IEEE Transactions on Multimedia (T-MM), 2021. (arxiv)

  • W. Jia, L. Li, A. Akhtar, Z. Li, and S. Liu, “Convolutional Neural Network-based Occupancy Map Accuracy Improvement for Video-based Point Cloud Compression”,
    IEEE Transactions on Multimedia (T-MM), 2021.

  • A. Akhtar, W. Gao, L. Li, Z. Li, X. Zhang, and S. Liu, “Point Cloud Geometry Prediction Across Spatial Scale using Deep Learning”,
    IEEE Visual Communication & Image Processing Conf (VCIP), Hong Kong, 2020.

  • A. Akhtar, J. Ma, R. Shafin, J. Bai, L. Li, Z. Li, L. Liu, “Low Latency Scalable Point Cloud Communication in VANETs using V2I Communication”
    IEEE International Conference on Communications (ICC), Shanghai, China. 2019.

  • A. Akhtar, B. Kathariya, Z. Li, “Low Latency Scalable Point Cloud Communication”
    IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan. 2019.

  • A. Akhtar, H. Arslan, “Downlink Resource Allocation and Packet Scheduling in Multi-Numerology Wireless Systems”
    IEEE Wireless Communications and Networking Conference (IEEE WCNC), 2018.

  • A. Akhtar, S. Coleri Ergen, “Directional MAC Protocol for IEEE 802.11ad WLANs”
    Ad Hoc Networks, 2018. (Protocol's website with explanation and open source code)

  • A. Akhtar, S. Coleri Ergen, “Efficient Network Level Beamforming Training for IEEE 802.11ad WLANs”
    International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2015), Chicago, Illinois, US. July 2015

Work Experience

  • Qualcomm. Current: (January. 2022 - Present)

    • Video-based dynamic mesh coding (V-DMC).

    • MPEG WG7 3DGCH: GSC, V-DMC, AI-PCC, V-PCC, G-PCC.

    • Inter-Frame and Intra-Frame Dynamic Point Cloud Compression using Deep Learning.

    • Deep Learning-based Point Cloud Interpolation.

    • Point Cloud Upsampling using Deep Learning.

  • Tencent. 3 months: (June. 2020 - Aug 2020)

    • Video-based Point Cloud Compression (V-PCC) Artifact Removal.

    • Point Cloud Geometry Prediction.

    • 3D Point Cloud Denoising.

    • End-to-End Point Cloud Compression (PCC).

  • HERE Technologies. 6 months: (June. 2019 - Nov 2019)

    • 2D Building Tracking, Segmentation, and Instance Segmentation.

    • 2D Facade Segmentation and Portal Detection.

    • 3D Point Cloud Semantic Segmentation.

Previous Projects

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Inter-prediction in Point Cloud Compression with Sparse Convolutional Networks (GitHub)

Internship work at Qualcomm.

  • Deep learning solution for inter-frame compression of high resolution point clouds.

  • Proposed I, B, and P-frame encoding framework.

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PU-Dense: Sparse Tensor-based Point Cloud Geometry Upsampling (Project Page) (GitHub)

Internship work at Qualcomm.

  • A sparse convolution-based point cloud upsampling solution that works on synthetic mesh-based dataset, sparse LiDAR-based point clouds, as well as dense high-resolution photo-realistic point clouds.

  • The current state-of-the-art in point cloud upsampling.

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Dynamic Point Cloud Interpolation.

Internship work at Qualcomm.

  • Deep learning-based point cloud interpolation framework for photorealistic dynamic point clouds.

  • Given two consecutive dynamic point cloud frames, the framework aims to generate intermediate frame(s) between them.

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Video-based Point Cloud Compression (V-PCC) Artifact Removal. (pdf)

Summer Internship work at Tencent.

  • Video-based Point Cloud Compression (V-PCC) standards introduces quantization at lower bitrate resulting in artifacts in the reconstructed point cloud.

  • We propose a deep learning-based V-PCC artifact removal framework.

  • We exploit the prior knowledge of the direction of quantization noise in V-PCC to learn both the direction as well as level of quantization noise by limiting the degree of freedom of the learned noise.

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Point Cloud Geometry Prediction Across Spatial Scale.

Summer Internship work at Tencent.

  • We propose a deep learning solution for point cloud geometry prediction scheme to upsample a lower Level-of-Detail (LoD) point cloud into a higher LoD point cloud.

  • We employ an octree-type upsampling solution to predict geometry across spatial scale.

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2D Penoptic Segmentation on street level imagery (SLI) from HERE True Drives.

Summer Internship at HERE Technologies.

  • 2D Building Facade Segmentation and Portal Detection.

  • 2D Building Tracking, Segmentation, and Instant Segmentation.

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Point Cloud Denoising.

Point clouds obtained from 3D scanners or by image-based reconstruction techniques are often corrupted with a significant amount of non-negligible noise.

  • We propose a two-stage deep neural network that takes in 3D point cloud data and outputs a denoised point cloud.

  • 1st stage: Outlier removal.

  • 2nd stage: Denoising surface noise.

  • We achieve state-of-the-art point cloud denoising results.

(Noisy point cloud on the left, Denoised point cloud on the right. JPEG 8i dataset)

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3D Semantic Segmentation on HERE True LiDAR Data.

Summer Internship at HERE Technologies.

  • Annotation of large scale outdoor LiDAR point cloud data.

  • Building Deep Learning Architecture for 3D Semantic Segmentation.

  • Feature abstraction from segmented 3D Point Cloud Data.

(Image on the left is from Semantic3D Dataset.)

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Realtime 3D Point Cloud Communication.

  • Joint source-channel coding for robustness to different channel conditions.

  • Adaptive Modulation and Coding schemes for point cloud broadcasting.

  • Adaptive Random Network Coding (ARNC) for scalable point cloud multicasting.

  • Low latency support for V2V as well as V2I communication

(Outdoor LiDAR data from Hesai shown on the left.)

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Mobile Edge Point Cloud Computing.

  • Registering infrastructure based and vehicle based point cloud submaps into one big point cloud.

  • Differentiating the static background from the dynamic live map.

(Google car collecting LiDAR data shown on the left.)

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Non-Deep Learning-based 3D Point Cloud Geometry Compression.

  • Binary Tree embedded Quad Tree (BTQT) source encoding.

  • Lossless point cloud geometry compression.

  • Error Resilient and Scalable point cloud source coding that is layered for different quality of service requirements.

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Deep Learning (Older Work)

  • Deep Super Resolution Networks for SIFT point repeatability.

    • Novel technique to super-resolve low resolution images into high resolution images while maintaining SIFT key points features.

  • Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks.

    • Designed a Convolutional Neural Network for Human Activity Detection using accelerometers and gyroscopes sensors from your cell-phone or wearable device. The results achieve 95% classification accuracy over 10 classes.

  • Market Trend Prediction for Cryptocurrency using Machine Learning.

    • Using traditional financial Technical Analysis (TA) coupled with deep learning techniques to predict Bitcoin price.

  • Machine learning applications in Wireless Communication and Network Science.

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5G and Beyond.

  • Resource allocation schemes for 5G heterogenous multi-numerology network.

  • Flexible waveform and numerology design for future cellular systems.

  • Adaptive CP size optimization in OFDM waveform.

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Directional mmWave Communication.

  • Directional MAC Protocol for IEEE 802.11ad WLANs. (PROJECT LINK)

  • Efficient Network Level Beamforming Training for IEEE 802.11ad WLANs.

  • Energy Efficient MAC Protocol with Localization scheme for Wireless Sensor Networks using Directional Antennas.

  • Multi-hop network neighbor discovery and beamforming using directional antennas in 802.11ad WLANs.

  • Optimization of link scheduling in directional wireless networks using Heuristic methods.

Education

  • Ph.D. Computer and Electrical Engineering. - August 2016 - 2022

    • University of Missouri - Kansas City, Missouri, USA. - January 2018 - June 2022

      • Advisor: Dr. Zhu Li

      • Research: Multimedia Computing & Communication.

    • University of South Florida, Tampa, Florida, USA. - August 2016 - December 2017

      • Research: 5G and Beyond, Machine Learning & Data Science.

  • Master of Science, Electrical Engineering - 2013-2015
    Koc University, Istanbul. Turkey
    Graduated: July 2015

  • Bachelor of Science, Electrical Engineering - 2008-2013
    Lahore University of Management Science, Lahore, Pakistan
    Graduated: June 2013

Teaching Experience

  • University of Missouri-Kansas City January. 2018 - 2021
    Department of Computer Science

    • ECE/CS 479/5582, Computer Vision - Fall 2021

    • ECE/CS 5578, Multimedia Communication - Spring 2021

    • ECE/CS 484, Digital Image Processing - Fall 2020

  • Koc University Aug. 2013 - July 2016
    Department of Electrical Engineering

    • ELEC 317, Microprocessors - Fall 2013

    • ENG 200, Probability for Engineers - Spring 2014

    • SCI 100, Natural Sciences - Fall 2014

    • ENG 200, Probability for Engineers - Spring 2015

    • SCI 100, Natural Sciences - Fall 2015

    • ENG 200, Probability for Engineers - Spring 2016

  • Lahore University of Management Science Aug. 2012- Jan. 2013
    Department of Electrical Engineering

    • EE 421, Digital System Design - Fall 2012