Written above & the author’s personal understanding Three-dimensional Gaussiansplatting (3DGS) is a transformative technology that has emerged in the fields of explicit radiation fields and computer graphics in recent years. This innovative method is characterized by the use of millions of 3D Gaussians, which is very different from the neural radiation field (NeRF) method, which mainly uses an implicit coordinate-based model to map spatial coordinates to pixel values. With its explicit scene representation and differentiable rendering algorithms, 3DGS not only guarantees real-time rendering capabilities, but also introduces an unprecedented level of control and scene editing. This positions 3DGS as a potential game-changer for next-generation 3D reconstruction and representation. To this end, we provide a systematic overview of the latest developments and concerns in the field of 3DGS for the first time.

Written previously, today we discuss how deep learning technology can improve the performance of vision-based SLAM (simultaneous localization and mapping) in complex environments. By combining deep feature extraction and depth matching methods, here we introduce a versatile hybrid visual SLAM system designed to improve adaptation in challenging scenarios such as low-light conditions, dynamic lighting, weakly textured areas, and severe jitter. sex. Our system supports multiple modes, including extended monocular, stereo, monocular-inertial, and stereo-inertial configurations. In addition, it also analyzes how to combine visual SLAM with deep learning methods to inspire other research. Through extensive experiments on public datasets and self-sampled data, we demonstrate the superiority of SL-SLAM in terms of positioning accuracy and tracking robustness.

You must remember, especially if you are a Teams user, that Microsoft added a new batch of 3DFluent emojis to its work-focused video conferencing app. After Microsoft announced 3D emojis for Teams and Windows last year, the process has actually seen more than 1,800 existing emojis updated for the platform. This big idea and the launch of the 3DFluent emoji update for Teams was first promoted via an official blog post. Latest Teams update brings FluentEmojis to the app Microsoft says the updated 1,800 emojis will be available to us every day

When the gossip started spreading that the new Windows 11 was in development, every Microsoft user was curious about how the new operating system would look like and what it would bring. After speculation, Windows 11 is here. The operating system comes with new design and functional changes. In addition to some additions, it comes with feature deprecations and removals. One of the features that doesn't exist in Windows 11 is Paint3D. While it still offers classic Paint, which is good for drawers, doodlers, and doodlers, it abandons Paint3D, which offers extra features ideal for 3D creators. If you are looking for some extra features, we recommend Autodesk Maya as the best 3D design software. like

0.Written in front&& Personal understanding that autonomous driving systems rely on advanced perception, decision-making and control technologies, by using various sensors (such as cameras, lidar, radar, etc.) to perceive the surrounding environment, and using algorithms and models for real-time analysis and decision-making. This enables vehicles to recognize road signs, detect and track other vehicles, predict pedestrian behavior, etc., thereby safely operating and adapting to complex traffic environments. This technology is currently attracting widespread attention and is considered an important development area in the future of transportation. one. But what makes autonomous driving difficult is figuring out how to make the car understand what's going on around it. This requires that the three-dimensional object detection algorithm in the autonomous driving system can accurately perceive and describe objects in the surrounding environment, including their locations,

1 Introduction Neural Radiation Fields (NeRF) are a fairly new paradigm in the field of deep learning and computer vision. This technology was introduced in the ECCV2020 paper "NeRF: Representing Scenes as Neural Radiation Fields for View Synthesis" (which won the Best Paper Award) and has since become extremely popular, with nearly 800 citations to date [1 ]. The approach marks a sea change in the traditional way machine learning processes 3D data. Neural radiation field scene representation and differentiable rendering process: composite images by sampling 5D coordinates (position and viewing direction) along camera rays; feed these positions into an MLP to produce color and volumetric densities; and composite these values ??using volumetric rendering techniques image; the rendering function is differentiable, so it can be passed

Written above & the author’s personal understanding: At present, in the entire autonomous driving system, the perception module plays a vital role. The autonomous vehicle driving on the road can only obtain accurate perception results through the perception module. The downstream regulation and control module in the autonomous driving system makes timely and correct judgments and behavioral decisions. Currently, cars with autonomous driving functions are usually equipped with a variety of data information sensors including surround-view camera sensors, lidar sensors, and millimeter-wave radar sensors to collect information in different modalities to achieve accurate perception tasks. The BEV perception algorithm based on pure vision is favored by the industry because of its low hardware cost and easy deployment, and its output results can be easily applied to various downstream tasks.

Project link written in front: https://nianticlabs.github.io/mickey/ Given two pictures, the camera pose between them can be estimated by establishing the correspondence between the pictures. Typically, these correspondences are 2D to 2D, and our estimated poses are scale-indeterminate. Some applications, such as instant augmented reality anytime, anywhere, require pose estimation of scale metrics, so they rely on external depth estimators to recover scale. This paper proposes MicKey, a keypoint matching process capable of predicting metric correspondences in 3D camera space. By learning 3D coordinate matching across images, we are able to infer metric relative