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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.