Productivity and safety in the construction industry have long been hindered by the many uncertainties and lack of awareness in the semi-controlled site environment. The digital twinning of construction objects aims at offering digital replicas with real-time, trustable evidence for automated monitoring, humancentric decision-making, or fully automatic cyber-physical systems. This paper revisits the pose estimation methods for the digital twinning of various on-site construction objects, including construction components, equipment, and humans. From a machine learning perspective, all the pose estimation methods can be categorized into four classes, i.e., filtering, supervised, reinforcement, and unsupervised. The inputs, processes, output, and target objects of each class are introduced with demonstrative cases. Comparisons on the pros and the cons of the methods reveal the best choices for digital twinning under different objectives, such as a safer site and more productive construction, as well as constraints such as pose accuracy, computational time, and overall cost. The complexities of digital twinning different construction objects are compared to explain the distribution of existing cases in the literature. Opportunities and possible research directions in the new era of AI and blockchain are recommended at the end.