Human-mouse hybrid brain appears! Stanford University 7-year research published in Nature

The first pilot and key article mainly introduces several commonly used coordinate systems in autonomous driving technology, and how to complete the correlation and conversion between them, and finally build a unified environment model. The focus here is to understand the conversion from vehicle to camera rigid body (external parameters), camera to image conversion (internal parameters), and image to pixel unit conversion. The conversion from 3D to 2D will have corresponding distortion, translation, etc. Key points: The vehicle coordinate system and the camera body coordinate system need to be rewritten: the plane coordinate system and the pixel coordinate system. Difficulty: image distortion must be considered. Both de-distortion and distortion addition are compensated on the image plane. 2. Introduction There are four vision systems in total. Coordinate system: pixel plane coordinate system (u, v), image coordinate system (x, y), camera coordinate system () and world coordinate system (). There is a relationship between each coordinate system,

StableDiffusion3’s paper is finally here! This model was released two weeks ago and uses the same DiT (DiffusionTransformer) architecture as Sora. It caused quite a stir once it was released. Compared with the previous version, the quality of the images generated by StableDiffusion3 has been significantly improved. It now supports multi-theme prompts, and the text writing effect has also been improved, and garbled characters no longer appear. StabilityAI pointed out that StableDiffusion3 is a series of models with parameter sizes ranging from 800M to 8B. This parameter range means that the model can be run directly on many portable devices, significantly reducing the use of AI

Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p

This paper explores the problem of accurately detecting objects from different viewing angles (such as perspective and bird's-eye view) in autonomous driving, especially how to effectively transform features from perspective (PV) to bird's-eye view (BEV) space. Transformation is implemented via the Visual Transformation (VT) module. Existing methods are broadly divided into two strategies: 2D to 3D and 3D to 2D conversion. 2D-to-3D methods improve dense 2D features by predicting depth probabilities, but the inherent uncertainty of depth predictions, especially in distant regions, may introduce inaccuracies. While 3D to 2D methods usually use 3D queries to sample 2D features and learn the attention weights of the correspondence between 3D and 2D features through a Transformer, which increases the computational and deployment time.

Some of the author’s personal thoughts In the field of autonomous driving, with the development of BEV-based sub-tasks/end-to-end solutions, high-quality multi-view training data and corresponding simulation scene construction have become increasingly important. In response to the pain points of current tasks, "high quality" can be decoupled into three aspects: long-tail scenarios in different dimensions: such as close-range vehicles in obstacle data and precise heading angles during car cutting, as well as lane line data. Scenes such as curves with different curvatures or ramps/mergings/mergings that are difficult to capture. These often rely on large amounts of data collection and complex data mining strategies, which are costly. 3D true value - highly consistent image: Current BEV data acquisition is often affected by errors in sensor installation/calibration, high-precision maps and the reconstruction algorithm itself. this led me to

Suddenly discovered a 19-year-old paper GSLAM: A General SLAM Framework and Benchmark open source code: https://github.com/zdzhaoyong/GSLAM Go directly to the full text and feel the quality of this work ~ 1 Abstract SLAM technology has achieved many successes recently and attracted many attracted the attention of high-tech companies. However, how to effectively perform benchmarks on speed, robustness, and portability with interfaces to existing or emerging algorithms remains a problem. In this paper, a new SLAM platform called GSLAM is proposed, which not only provides evaluation capabilities but also provides researchers with a useful way to quickly develop their own SLAM systems.

Please note that this square man is frowning, thinking about the identities of the "uninvited guests" in front of him. It turned out that she was in a dangerous situation, and once she realized this, she quickly began a mental search to find a strategy to solve the problem. Ultimately, she decided to flee the scene and then seek help as quickly as possible and take immediate action. At the same time, the person on the opposite side was thinking the same thing as her... There was such a scene in "Minecraft" where all the characters were controlled by artificial intelligence. Each of them has a unique identity setting. For example, the girl mentioned before is a 17-year-old but smart and brave courier. They have the ability to remember and think, and live like humans in this small town set in Minecraft. What drives them is a brand new,

Written above & The author’s personal understanding is that image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have attracted attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art 3D reconstruction techniques, including generating novel, unseen views. An overview of recent developments in Gaussian splash methods is provided, including input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this field and the numerous opportunities to enhance 3D reconstruction methods, a thorough examination of the algorithm seems crucial. Therefore, this study provides a comprehensive overview of recent advances in Gaussian scattering. (Swipe your thumb up