Real-time 3D Perception of Scene with Monocular Camera

Depth is a vital prerequisite for the fulfillment of various tasks such as perception, navigation, and planning. Estimating depth using only a single image is a challenging task since the analytic mapping is not available between the intensity image and its depth where the features cue of the context is usually absent in the single image. Furthermore, most current researchers rely on the supervised Learning approach to handle depth estimation. Therefore, the demand for recorded ground truth depth is important at the training time, which is actually tricky and costly. This study presents two approaches (unsupervised learning and semi-supervised learning) to learn the depth information using only a single RGB-image. The main objective of depth estimation is to extract a representation of the spatial structure of the environment and to restore the 3D shape and visual appearance of objects in imagery

Perception Through Image Processing Applications for Situation Awareness

This paper introduces a concept for the perception level of situation awareness through image processing applications. The perception of the information from the environment is lacking to achieve highly autonomous vehicles. This approach mainly focuses on collecting information from the environment using camera sensors. The frames from the camera are processed using multiple image processing algorithms, outputs converted into CAN message, and sent to the expert system. CE-Box custom hardware that consists of a Raspberry Pi 3b model counted with PiCAN 2 used to implement and evaluate this approach. The goal of this paper is to provide a conceptual method to make decisions based on the extracted information from the environment using image processing algorithms

3D Object Detection based on Unsupervised Depth Estimation

Estimating depth and detection of object instances in 3D space is fundamental in autonomous navigation, localization, and mapping, robotic object manipulation, and augmented reality. RGB-D images and LiDAR point clouds are the most illustrative formats of depth information. However, depth sensors offer many shortcomings, such as low effective spatial resolutions and capturing of a scene from a single perspective. The thesis focuses on reproducing denser and comprehensive 3D scene structure for given monocular RGB images using depth and 3D object detection. The first contribution of this thesis is the pipeline for the depth estimation based on an unsupervised learning framework. This thesis proposes two architectures to analyze structure from motion and 3D geometric constraint methods. The proposed architectures trained and evaluated using only RGB images and no ground truth depth data. The architecture proposed in this thesis achieved better results than the state-of-the-art methods. The second contribution of this thesis is the application of the estimated depth map, which includes two algorithms: point cloud generation and collision avoidance. The predicted depth map and RGB image are used to generate the point cloud data using the proposed point cloud algorithm. The collision avoidance algorithm predicts the possibility of collision and provides the collision warning message based on decoding the color in the estimated depth map. This algorithm design is adaptable to different color map with slight changes and perceives collision information in the sequence of frames. Our third contribution is a two-stage pipeline to detect the 3D objects from a monocular image. The first stage pipeline used to detect the 2D objects and crop the patch of the image and the same provided as the input to the second stage. In the second stage, the 3D regression network train to estimate the 3D bounding boxes to the target objects. There are two architectures proposed for this 3D regression network model. This approach achieves better average precision than state-of-theart for truncation of 15% or fully visible objects and lowers but comparable results for truncation more than 30% or partly/fully occluded objects

3D Object Detection based on Unsupervised Depth Estimation

Estimating depth and detection of object instances in 3D space is fundamental in autonomous navigation, localization, and mapping, robotic object manipulation, and augmented reality. RGB-D images and LiDAR point clouds are the most illustrative formats of depth information. However, depth sensors offer many shortcomings, such as low effective spatial resolutions and capturing of a scene from a single perspective. The thesis focuses on reproducing denser and comprehensive 3D scene structure for given monocular RGB images using depth and 3D object detection. The first contribution of this thesis is the pipeline for the depth estimation based on an unsupervised learning framework. This thesis proposes two architectures to analyze structure from motion and 3D geometric constraint methods. The proposed architectures trained and evaluated using only RGB images and no ground truth depth data. The architecture proposed in this thesis achieved better results than the state-of-the-art methods. The second contribution of this thesis is the application of the estimated depth map, which includes two algorithms: point cloud generation and collision avoidance. The predicted depth map and RGB image are used to generate the point cloud data using the proposed point cloud algorithm. The collision avoidance algorithm predicts the possibility of collision and provides the collision warning message based on decoding the color in the estimated depth map. This algorithm design is adaptable to different color map with slight changes and perceives collision information in the sequence of frames. Our third contribution is a two-stage pipeline to detect the 3D objects from a monocular image. The first stage pipeline used to detect the 2D objects and crop the patch of the image and the same provided as the input to the second stage. In the second stage, the 3D regression network train to estimate the 3D bounding boxes to the target objects. There are two architectures proposed for this 3D regression network model. This approach achieves better average precision than state-of-theart for truncation of 15% or fully visible objects and lowers but comparable results for truncation more than 30% or partly/fully occluded objects

3D Object Detection based on Unsupervised Depth Estimation

Estimating depth and detection of object instances in 3D space is fundamental in autonomous navigation, localization, and mapping, robotic object manipulation, and augmented reality. RGB-D images and LiDAR point clouds are the most illustrative formats of depth information. However, depth sensors offer many shortcomings, such as low effective spatial resolutions and capturing of a scene from a single perspective. The thesis focuses on reproducing denser and comprehensive 3D scene structure for given monocular RGB images using depth and 3D object detection. The first contribution of this thesis is the pipeline for the depth estimation based on an unsupervised learning framework. This thesis proposes two architectures to analyze structure from motion and 3D geometric constraint methods. The proposed architectures trained and evaluated using only RGB images and no ground truth depth data. The architecture proposed in this thesis achieved better results than the state-of-the-art methods. The second contribution of this thesis is the application of the estimated depth map, which includes two algorithms: point cloud generation and collision avoidance. The predicted depth map and RGB image are used to generate the point cloud data using the proposed point cloud algorithm. The collision avoidance algorithm predicts the possibility of collision and provides the collision warning message based on decoding the color in the estimated depth map. This algorithm design is adaptable to different color map with slight changes and perceives collision information in the sequence of frames. Our third contribution is a two-stage pipeline to detect the 3D objects from a monocular image. The first stage pipeline used to detect the 2D objects and crop the patch of the image and the same provided as the input to the second stage. In the second stage, the 3D regression network train to estimate the 3D bounding boxes to the target objects. There are two architectures proposed for this 3D regression network model. This approach achieves better average precision than state-of-theart for truncation of 15% or fully visible objects and lowers but comparable results for truncation more than 30% or partly/fully occluded objects