# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 2 Related Work  
In this section, we present pertinent prior research encompassing studies on target recognition and segmentation, feature extraction, and matching.  
Target recognition and segmentation: The field of object recognition and segmentation techniques can be categorized into two main approaches: traditional artificial design features and deep neural network-based methods. With the rapid advancements in neural network technology, CNN-based algorithms have demonstrated superior capabilities compared to artificial design features in various domains such as object detection and semantic segmentation [13; 14]. Notably, the Fast/Faster R-CNN [15; 16] and FCN methods for semantic segmentation exhibit remarkable flexibility and robustness. The SegNet model utilizes a symmetrical encoder-decoder architecture to maintain feature positions throughout downsampling and restore them during upsampling, thus improving the output resolution of the model. Void convolution enlarges the receptive field of output elements by incorporating \"holes\" into the convolution kernel while keeping the parameter count unchanged. The RefineNet model exploits features at different levels for gathering image information using various sampling techniques, optimizes utilization of global-level features, and integrates long-range residual connections to improve semantic segmentation. Deep Lab v3 integrates a batch normalization layer within its network architecture and utilizes parallel as well as serial hole convolution modules for enabling multi-scale object classification [17; 18; 19]. In this study, we employed a multitasking framework that facilitates target identification and segmentation, thereby incorporating a comprehensive loss function encompassing target classification error, regression error, and image segmentation error.  
Feature extraction and matching: Vision-based localization methods commonly utilize point, line, and surface features. The algorithms frequently employed for feature point detection include Harris, FAST, SIFT, etc. Moreover, there are also local feature-based detection techniques such as Log edge detection, LBP, HOG, etc. [20; 21; 22; 23]. Although these methods are interpretable and easy to understand and debug, they lack generality and flexibility. They are more suitable for small datasets with obvious scene features. Recently, several deep learning-based feature detection methods have been proposed. For example, [PERSON] et al. introduced the Super Point feature extraction method which is a self-supervised approach that trains the network to extract feature points and compute descriptors. Multi PoseNet can simultaneously handle human detection and keypoint localization by utilizing a pose residual network (PRN) to implement a novel pose allocation technique [24; 25]. These methods automatically learn features with strong versatility and flexibility but lack interpretability; thus they are more suitable for scenes with large datasets and complex features. Our study proposes a novel hybrid feature detection approach that effectively integrates semantic information into BRIEF descriptors, thereby harnessing the combined strengths of both methodologies.