###### Abstract  
Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving sensors such as LiDAR, stereo cameras, RGB-D, RADAR, etc. The existing review articles on 3D object detection techniques are found to be focusing on either a singular modality (e.g., only LiDAR point cloud-based) or a singular application field (e.g., autonomous vehicle navigation). However, to the best of our knowledge, there is no review paper that discusses the applicability of 3D object detection techniques in other fields such as agriculture, robot vision or human activity detection. This study analyzes both singular and multimodal techniques of 3D object detection techniques applied in different fields. A critical analysis comprising strengths and weaknesses of the 3D object detection techniques is presented. The aim of this study is to facilitate future researchers and practitioners to provide a holistic view of 3D object detection techniques. The critical analysis of the singular and multimodal techniques is expected to help the practitioners find the appropriate techniques based on their requirement.  
3D object detection; singular technique; multimodal technique +
Footnote †: journal: _Applied_  
**Citation**: [PERSON].  
detection is being applied in medical sector for internal organ disease detection. In the medical sector, 3D CNN is being used on 3D images obtained by Computed Tomography (CT), Diffusion tensor imaging (DTI), magnetic resonance imaging (MRI), Functional magnetic resonance imaging and Ultrasound [17]. But medical imaging differs in terms of data representation and is difficult to compare with other domains.  
Since computers with GPUs are more available now, it has enhanced the scope of 3D detection with deep learning. Several review papers are found on 3D object detection. For example, [PERSON] et al., 2021, presented a survey paper on 3D object detection [18]. This paper solely focuses on LiDAR-generated point cloud-based 3D object detection. Moreover, only self-driving vehicle-related methods are discussed in this paper. [PERSON] et al., 2021, published another survey paper on current trends in 3D object detection [19]. This survey exclusively covers LiDAR-based methods and focuses on the self-driving field. [PERSON] et al., 2019, published a survey article on 3D object detection techniques obtained by sensors other than LiDAR. This article discusses some fusion methods as well [20]. This research emphasizes 3D detection techniques relevant to autonomous driving. After carefully studying the \"3D object detection\"-related review papers published in Scopus and Web of Science journals, the contents of the most relevant review papers are noted in Table 1. This visual presentation has paved the way to existing research gaps and new research scopes on 3D object detection techniques. Table 1 contains the relevant review papers.  
Three-dimensional object detection techniques are recently gaining popularity due to the necessity of object shape and orientation estimation in real-world space. However, Table 1 clearly shows that, a comprehensive review paper on 3D object detection is still lacking which analyses both singular and multimodal techniques in recently popular application domains. This review article is written with a view to reducing this gap to some extent.