Dingding Cai

I am a doctoral student at Computer Vision Group, Tampere University, Finland, where I am working on 3D computer vision and machine learning under the supervision of Prof. Esa Rahtu and Prof. Janne Heikkilä.

I accomplished my Master's degree in Data Engineering and Signal Processing at Tampere University of Technology with Prof. Joni Kämäräinen and Prof. Ke Chen in 2017. Prior to my doctoral study, I was a computer vision algorithm engineer working in Misshfresh Limited, Shenzhen, China.

Email (dingding.cai@tuni.fi)  /  Google Scholar  /  CV

profile photo
Research

I'm interested in various 2D/3D computer vision tasks, in particular, 3D scene understanding, 6D object pose estimation and tracking.

GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
Project page / Paper / Code

We propose an end-to-end framework for locating and estimating the 6D pose of novel objects. The proposed apporach leverages object co-segmentation and 3D Gaussian Splatting for generalizable object pose estimation.

MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
SCIA, 2023
Paper

We propose a practical self-supervised domain adaptation approach that takes advantage of real RGB(-D) data without needing real pose labels.

SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
3DV, 2022
Paper / Code

We present an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D requires neither the 3D CAD model of the object nor any prior knowledge of the symmetries.

OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
CVPR, 2022
Project page / Paper / Code

We propose a universal object 6D pose estimation model, called OVE6D, purely trained on synthetic 3D objects from ShapeNet and generalizing remarkably well to unseen objects without needing any parameter optimization.

Convolutional Low-resolution Fine-grained Classification
Dingding Cai, Ke Chen, Yanlin Qian, Joni-Kristian Kämäräinen
Pattern Recognition Letters, 2019
Paper / Code

We propose a novel resolution-aware deep model which combines convolutional image super-resolution and convolutional fine-grained classification into a single model in an end-to-end manner.