A Survey on Rigid Object Pose Estimation

PhD Qualifying Examination


Title: "A Survey on Rigid Object Pose Estimation"

by

Miss Na FAN


Abstract:

Rigid object pose estimation is a fundamental task in computer vision with 
applications across various domains such as robotics, augmented reality 
(AR), and autonomous driving. Given single or combinations of input types 
from RGB images, depth images, point clouds, and text prompts, along with an 
optional CAD model of the target object, pose estimation aims to determine 
the relative transformation (rotation and translation) of the object's 
coordinate frame with respect to the camera's coordinate frame and 
potentially estimate the object size. The field has rapidly evolved from 
traditional methods relying on handcrafted features to learning-based 
approaches, progressing from instance-level to category-level pose 
estimation and further to unseen pose estimation. More recently, with 
pre-trained large vision and language models, it has advanced to handle more 
challenging open-vocabulary object pose estimations. Persistent challenges 
include the reliance on annotated data, robustness in complex scenarios 
(e.g., cluttered environments, occlusions), handling objects with special 
properties (e.g., symmetry, transparency), inference efficiency, and 
generalization to new objects.

This survey begins by introducing the problem definition of rigid object 
pose estimation and the evaluation metrics. Subsequently, we categorize 
existing methods into instance-level, category-level, and unseen object pose 
estimations, introducing the related datasets and methods at each level. We 
categorize the methods by their approaches and input types and introduce in 
detail the representative methods, analyzing the technical details, 
strengths, and weaknesses. In conclusion, we summarize these methods and 
discuss potential future directions in the field of rigid object pose 
estimation.


Date:                   Friday, 6 December 2024

Time:                   1:00pm - 3:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Qifeng Chen (Supervisor)
                        Prof. Qiong Luo (Chairperson)
                        Dr. Brian Mak
                        Dr. Dan Xu