Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
publications
DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds
Published in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2026
DiffCom presents a diffusion-based point cloud compression framework that decouples storage-efficient sparse priors from reconstruction latents, reducing latent redundancy while preserving geometric fidelity.
Recommended citation: Xiaoge Zhang, Zijie Wu, Mingtao Feng, Mehwish Nasim, Saeed Anwar, & Ajmal Mian. (2026). DiffCom: Decoupled sparse priors guided diffusion compression for point clouds. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) .
Download Paper
FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds
Published in IEEE International Conference on Multimedia and Expo (ICME), 2026
FLaTEC presents a frequency-aware LiDAR point cloud compression framework that projects voxel features into compact triplanes and disentangles low- and high-frequency components for flexible bitrate allocation.
Recommended citation: Xiaoge Zhang, Zijie Wu, Mingtao Feng, Zichen Geng, Mehwish Nasim, Saeed Anwar, & Ajmal Mian. (2026). FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds. 2026 IEEE International Conference on Multimedia and Expo (ICME).
Download Paper
MAGiC-NeRF: Multimodal Generative Priors and Conditional Flow for Extreme NeRF Compression
Published in Preprint, 2026, 2026
MaGic-NeRF introduces an extreme NeRF compression framework that distills trained radiance fields into compact triplanes and reconstructs them from highly compressed multimodal semantic priors.
Recommended citation: Xiaoge Zhang, Zijie Wu, Mingtao Feng, Lei Yang, Zichen Geng, Saeed Anwar, & Ajmal Mian. (2026). MAGiC-NeRF: Multimodal Generative Priors and Conditional Flow for Extreme NeRF Compression.
PGMS: Pyramidal Gaussian Mixture Splatting for 3DGS Compression
Published in Preprint, 2026, 2026
PGMS introduces a plug-and-play 3DGS compression framework that reformulates dense Gaussian primitives into a rate-controllable pyramidal mixture representation, explicitly reducing the joint redundancy of geometry and appearance while preserving fine structural and visual details.
Recommended citation: Xiaoge Zhang, Zijie Wu, Mingtao Feng, Saeed Anwar, & Ajmal Mian. (2026). PGMS: Pyramidal Gaussian Mixture Splatting for 3DGS Compression.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.