Hello, I'm Tuan Duc Ngo

I am a second-year PhD student in Computer Science of UMass Amherst, USA, advised by Prof. Evangelos Kalogerakis and Prof. Chuang Gan. Prior to that, I was an AI Research Resident at VinAI Research. I received my B.E. degree in Computer Engineering from the Ho Chi Minh City University of Technology, Vietnam. Email: ductuan.ngo99 (at) gmail (dot) com


News

  • Feb 2025: 4Real-Video is accepted to CVPR 2025.
  • Jan 2025: DELTA is accepted to ICLR 2025, and the code is also released (Github)
  • May 2024: I joined Snap Research as a Research Intern.
  • Feb 2024: Open3DIS is accepted to CVPR 2024. We have also released the code.
  • Jul 2023: GaPro is accepted to ICCV 2023. We have also released the code.
  • Feb 2023: ISBNet is accepted to CVPR 2023. We have also released the code.
  • Jul 2022: GeoFormer is accepted to ECCV 2022. We have also released the code.

Publications

4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion
DELTA: Dense Efficient Long-range 3D Tracking for Any video

DELTA: Dense Efficient Long-range 3D Tracking for Any video

ICLR, 2025

Capture the dense, long-range, 3D point trajectories from casual videos in a feed-forward manner

GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers

GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers

Tuan Duc Ngo*, Binh-Son Hua, Khoi Nguyen
ICCV, 2023

Tackle the box-supervised 3D point cloud instance segmentation by using Gaussian Processes to generate pseudo labels

ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution

ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution

Tuan Duc Ngo*, Binh-Son Hua, Khoi Nguyen
CVPR, 2023

Introduce an efficient sampling strategy and propose leveraging the bounding box as a geometric cue for the 3D point cloud instance segmentation

Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance Segmenter

Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance Segmenter

Tuan Duc Ngo*, Khoi Nguyen
ECCV, 2022

Propose a new task, Few-shot 3D point cloud instance segmentation, and introduce a geodesic-based 3D instance segmenter

GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution

GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution

PeerJ, 2021

Propose a new monocular 3D detection framework leveraging the ground plane model and depth-adaptive convolution