Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes...Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF data.Given this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this area.We summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep learning.We analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed requirements.In addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.展开更多
Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentati...Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF rendering.However,there is a contradiction between spatial and angular resolution during the LF image acquisition period.To overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian models.Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities.In this paper,the present approach can mainly divided into conventional methods and deep learning-based methods.We discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),respectively.Subsequently,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets.Finally,we discuss the potential innovations of the LFSR to propose the progress of our research field.展开更多
基金supported by the National Key R&D Program of China(2022YFC3803600)the National Natural Science Foundation of China(62372023)the Open Fund of the State Key Laboratory of Software Development Environment,China(SKLSDE-2023ZX-11).
文摘Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF data.Given this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this area.We summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep learning.We analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed requirements.In addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.
基金supported by the National Key R&D Program of China(2022YFC3803600)the National Natural Science Foundation of China(62372023)the Open Fund of the State Key Laboratory of Software Development Environment,PR China(SKLSDE-2023ZX-11)。
文摘Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF rendering.However,there is a contradiction between spatial and angular resolution during the LF image acquisition period.To overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian models.Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities.In this paper,the present approach can mainly divided into conventional methods and deep learning-based methods.We discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),respectively.Subsequently,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets.Finally,we discuss the potential innovations of the LFSR to propose the progress of our research field.
