Image Object Detection Depth Estimation Semantic Segmentation We propose a cnn which takes as inputs a single rgb image and a sparse depth image, and returns the corresponding semantic segmentation image and a dense depth map in an end to end manner. In this paper, we propose a novel joint task recursive learn ing (trl) framework for the closing loop semantic segmentation and monocular depth estimation tasks. trl can recursively refine the re sults of both tasks through serialized task level interactions.

Image Object Detection Depth Estimation Semantic Segmentation To solve this problem, we propose a method that improves segmentation quality with depth estimation on rgb images. specifically, we estimate depth information on rgb images via a depth estimation network, and then feed the depth map into the cnn which is able to guide the semantic segmentation. In this paper, we introduce a hybrid convolutional network that integrates depth estimation and semantic segmentation into a unified framework. we propose to build a model where the features extracted are suitable for both tasks, thus leading to an improved accuracy in the estimated information. In this paper, we propose a depth estimation model based on semantic segmentation, which combines semantic segmentation modules and depth estimation modules to share parameters, and uses the semantic segmentation information to guide the depth estimation task to learn additional information. This research presents a comprehensive system encompassing semantic segmentation and depth estimation for 360 degree images. it introduces effective methodologies to tackle the challenges associated with depth estimation in panoramic imagery and enhance the precision of semantic segmentation.

Exploiting Depth From Single Monocular Images For Object Detection And In this paper, we propose a depth estimation model based on semantic segmentation, which combines semantic segmentation modules and depth estimation modules to share parameters, and uses the semantic segmentation information to guide the depth estimation task to learn additional information. This research presents a comprehensive system encompassing semantic segmentation and depth estimation for 360 degree images. it introduces effective methodologies to tackle the challenges associated with depth estimation in panoramic imagery and enhance the precision of semantic segmentation. In this paper, we first introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks through an analysis of the imaging process, then propose a semantic object segmentation and depth estimation network (sosd net) based on the objectness assumption. In the image point cloud fusion module, this study proposes a two dimensional depth information recovery method that uses the semantic segmentation results to guide the downsampling of depth estimation. We present a depth estimation framework designed to ex plicitly consider the mutual benefits between two neighbor ing computer vision tasks of self supervised depth estima tion and semantic segmentation. Our proposed sosd net architecture leverages a shared encoder backbone and a decoder for semantic feature, common representation and depth feature, followed by depth to semantic and semantic to depth modules to learn semantic segmentation and depth estimation from a single image, respectively.