Photoshop鼠绘出非常可爱的3D圣诞雪人效果

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.

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

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,

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.

ChatGPT has injected a dose of chicken blood into the AI ​​industry, and everything that was once unthinkable has become basic practice today. Text-to-3D, which continues to advance, is regarded as the next hotspot in the AIGC field after Diffusion (images) and GPT (text), and has received unprecedented attention. No, a product called ChatAvatar has been put into low-key public beta, quickly garnering over 700,000 views and attention, and was featured on Spacesoftheweek. △ChatAvatar will also support Imageto3D technology that generates 3D stylized characters from AI-generated single-perspective/multi-perspective original paintings. The 3D model generated by the current beta version has received widespread attention.

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

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

For autonomous driving applications, it is ultimately necessary to perceive 3D scenes. The reason is simple. A vehicle cannot drive based on the perception results obtained from an image. Even a human driver cannot drive based on an image. Because the distance of objects and the depth information of the scene cannot be reflected in the 2D perception results, this information is the key for the autonomous driving system to make correct judgments on the surrounding environment. Generally speaking, the visual sensors (such as cameras) of autonomous vehicles are installed above the vehicle body or on the rearview mirror inside the vehicle. No matter where it is, what the camera gets is the projection of the real world in the perspective view (PerspectiveView) (world coordinate system to image coordinate system). This view is very similar to the human visual system,