RGB-based category-level 6D pose estimation methods have faced significant challenges in achieving real-time performance, primarily due to the design of two-stage pipeline. To address this issue, we propose a novel end-to-end pipeline named EPCPE. In detail, we first extract implicit rotation features via Large Visual Model (LVM), and then adaptively obtain pose-specific features with a fined-tuned Lite Feature Extractor. Finally, we introduce a novel Pose Decoder with two parallel branches, enabling simultaneous 6D pose estimation and 2D object detection. We also propose a novel rotation loss function to further enhance the performance. Extensive experiments on the CAMERA25 and REAL275 datasets demonstrate that our pipeline is concise, achieves state-of-the-art (SOTA) and real-time performance.

Maximization of mutual information (MI) (MMI) has a wide application in multimodal image registration. The conventional MMI algorithm is based on the statistics derived from image sampling and does not use spatial features. In this paper, we proposed an improved MMI registration algorithm that combines the spatial information through a feature-based selection mechanism. A Harris corner filter is chosen to classify the pixels. The classification results are used to focus sample selection, joint entropy estimation, and weighted MI information calculation. Although features play an important role, conventional feature matching is not used, which removes an important challenge to the use of feature-based information. The advantages of the proposed algorithm are improved robustness by incorporation of spatial features and significant reduction in computing time for registration. Experiments with challenging synthetic images and multimodal airborne infrared images are provided to demonstrate the improvement.

Registration of maps and airborne or satellite images is an important problem for tasks such as map updating and change detection. This is a difficult problem because map features such as roads and buildings may be mislocated and features extracted from images may not correspond to map features, which is the challenge for the traditional feature-based registration algorithm. Our algorithm obtains a general global registration of maps and images by applying statistical techniques to map and image features. Finer analysis can then be used to find changes and local mismatches. The Maximization of Mutual Information (MMI) technique has proven to be very robust in image-to-image registration. This paper extends the MMI technique to the map-to-image registration problem through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability of being information rich and evaluates the accuracy and robustness in series experiments. The advantages and limitations are discussed in detail. The experimental results demonstrate map and image registration.

Web browsers have evolved from a single-principal platform on which one site is browsed at a time into a multi-principal platform on which data and code from mutually distrusting sites interact programmatically in a single page at the browser. Today's "Web 2.0" applications (or mashups) offer rich services, rivaling those of desktop PCs. However, the protection andcommunication abstractions offered by today's browsers remain suitable onlyfor a single-principal system--either no trust through completeisolation between principals (sites) or full trust by incorporating third party code as libraries. In this paper, we address this deficiency by identifying and designing the missing abstractions needed for a browser-based multi-principal platform. We have designed our abstractions to be backward compatible and easily adoptable. We have built a prototype system that realizes almost all of our abstractions and their associated properties. Our evaluation shows that our abstractions make it easy to build more secure and robust client-side Web mashups and can be easily implemented with negligible performance overhead.