With the recent increase in space missions and the proliferation of space debris，the perception of noncooperative objects has become a critical issue for on-orbit servicing and space situational awareness. Noncooperative objects are objects in space that lack any cooperative markers，communication channels，or navigation aids，such as defunct satellites，orbital debris，small debris，or unknown spacecraft. Identifying such objects and estimating their six-degree-of-freedom pose is crucial for various space activities，including debris removal，rendezvous and docking，satellite servicing，and space security. Reliable identification provides prior semantic and structural information for subsequent pose estimation，and accurate pose estimation supports autonomous navigation，capture，and manipulation. Therefore，progress in this field is not only of scientific value but also of practical importance for ensuring the safety and sustainability of space operations. This study comprehensively reviews the progress of the latest research on noncooperative space object recognition and pose estimation on the basis of vision and deep learning. Unlike traditional computer vision methods that rely heavily on handcrafted features and geometric modeling，deep learning methods demonstrate high robustness，adaptability，and generalization advantages in complex space environments，such as those with weak textures，drastic illumination variations，rapid pose changes，and partial occlusion. We categorize existing research into two major areas，namely，object recognition and pose estimation，and systematically analyze representative methods along with their strengths，limitations，and applicability. For object recognition，three main research directions are identified. The first is multimodal visual fusion，which integrates data from multiple sensors，such as RGB cameras，infrared imaging，depth maps，or radar. By leveraging complementary features across modalities，these methods can overcome the limitations of single-sensor systems，particularly in low visibility or with incomplete imagery. However，multimodal fusion often incurs additional computational overhead and sensor dependency. The second research direction is object detection and segmentation methods. Powered by deep models，such as you only look once （YOLO），single-shot multibox detector，faster region-based convolutional neural network，and mask region-based convolutional neural network，these methods have substantially improved spatial object localization and fine-grained analysis capabilities. However，these methods typically require large-scale annotated datasets and often struggle to identify very small or distant objects. The third direction is transfer learning and few-shot learning methods，which mitigate data scarcity by adopting pretrained models or meta-learning frameworks. These methods have produced promising results in identifying satellites or debris with limited sample sizes，although domain transfer between synthetic and real images remains a challenge. For pose estimation，this study reviews four major categories of methods. First，direct regression methods regard pose estimation as an end-to-end mapping problem and are highly effective when dealing with symmetric or highly variable objects. Second，keypoint detection methods combine landmark localization with geometric constraints to achieve robust performance. Third，unsupervised and domain adaptation methods reduce the reliance on expensive labeled data and address the gap between synthetic and real domains through reconstruction losses，contrastive learning，or adversarial adaptation. Finally，multimodal fusion methods combine visual data with LiDAR，radar，or infrared sensing to improve robustness in extreme environments，but the complexity of the integration limits their widespread application. In addition to methodological analysis，this study summarizes public datasets，such as SPEED，SPEED+，URSO，and BUAA-SID，and self-constructed datasets. Evaluation metrics，such as position error，pose error，mean average precision，intersection over union，and inference speed，are summarized to provide strong data support for researchers. For spatial object recognition，the traditional principal component analysis feature extraction method combined with random forest classification achieves an accuracy of approximately 89.13%. The deep convolutional neural network （DCNN） approach substantially improves recognition performance，with its accuracy increments ranging from 4.31% to 15.03%. By further employing data augmentation strategies，DCNN’s recognition accuracy is as high as 99.90%，demonstrating exceptional generalization and robustness. In the pose estimation task，with the SPEED dataset as a benchmark，the traditional perspective-n-point algorithm achieves a pose angle error of 8.74°，with its performance degrading by over 40% in low-resolution imagery. By contrast，the deep learning-based DSOAE-Net model reduces the pose angle error to 7.53° and maintains stable accuracy in low-resolution scenarios，demonstrating high environmental adaptability and robustness. Comparative analysis shows that deep learning-based methods outperform traditional methods in terms of robustness to illumination variations，occlusions，and structural ambiguity. Multimodal fusion and domain adaptation strategies are particularly effective in improving cross-domain generalization capabilities. In sum，this study summarizes the main challenges that face object recognition and pose estimation methods and proposes future development directions to provide a reference for technical research and engineering applications in this field. The methods，datasets，and evaluation metrics mentioned are linked at https://github.com/viskyll/openResource/tree/main.

non-cooperative space target  /  target recognition  /  pose estimation  /  vision  /  deep learning  /  multimodal visual fusion  /  direct regression  /  key point detection