点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心，作者：张峻川 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 一、写在前面&背景 神经渲染（NERF/3DGS）引发了三维重建技术的革命，目前已经在辅助驾驶/具身智能领域得到大量应用。NERF和3DGS使用神经网络表达空间，其在新视角合成 方面的优越表现直击辅助驾驶/具身智能仿真的一大痛点：传感器仿真。如果这一类深度学习技术能够推广应用，就能够很大程度上解决传统计算机图形学渲染出的 图像缺乏真实性的问题，可以广泛应用在算法的闭环测试和训练中。 目前已经有一些研究项目在围绕NERF和3DGS技术打造全新的面向闭环测试的仿真框架。然而完全新开发一个仿真框架，使其具有现行场景仿真软件类似的功能将 会有巨大的工作量。因此另一个应用神经渲染新技术的思路是：将NERF和3DGS训练出的模型嵌入到现有仿真软件的框架中去，在保证实时渲染的前提下，同时能 够应用仿真软件已有的3D数字资产和算法接口等工具链。 在N ...

Core Viewpoint - The article announces the launch of the "Heart of Autonomous Driving" project and paper guidance, aimed at assisting students facing challenges in their research and development efforts in the field of autonomous driving [1]. Group 1: Project and Guidance Overview - The project aims to provide support for students who encounter difficulties in their research, such as environmental configuration issues and debugging challenges [1]. - Last year's outcomes were positive, with several students successfully publishing papers in top conferences like CVPR and ICRA [1]. Group 2: Guidance Directions - **Direction 1**: Focus on multi-modal perception and computer vision, end-to-end autonomous driving, large models, and BEV perception. The guiding teacher has published over 30 papers in top AI conferences with a citation count exceeding 6000 [3]. - **Direction 2**: Emphasis on 3D Object Detection, Semantic Segmentation, Occupancy Prediction, and multi-task learning based on images or point clouds. The guiding teacher is a top-tier PhD with multiple publications in ECCV and CVPR [5]. - **Direction 3**: Concentration on end-to-end autonomous driving, OCC, BEV, and world model directions. The guiding teacher is also a top-tier PhD with contributions to several mainstream perception solutions [6]. - **Direction 4**: Focus on NeRF / 3D GS neural rendering and 3D reconstruction. The guiding teacher has published four CCF-A class papers, including two in CVPR and two in IEEE Transactions [7].

Core Insights - The article discusses the latest trends in the field of computer vision, highlighting three major research directions that are gaining traction as of 2025 [3][4]. Group 1: Major Research Directions - The three prominent areas identified are: 1. Multi-view and sensor 3D technology, which has evolved from 2D rendering to more complex 3D evaluations, significantly influenced by the introduction of NeRF in 2020 [5]. 2. Image and video synthesis, which has become a focal point for presenting environmental information more accurately, reflecting advancements in the ability to analyze and generate multimedia content [6]. 3. Multimodal learning, which integrates visual, linguistic, and reasoning capabilities, indicating a trend towards more interactive and comprehensive AI systems [7][8]. Group 2: Conference Insights - The CVPR 2025 conference has seen a 13% increase in paper submissions, with a total of 13,008 submissions and an acceptance rate of 22.1%, indicating a highly competitive environment [3]. - The conference emphasizes the importance of diverse voices in the research community, ensuring that every paper, regardless of the author's affiliation, is given equal consideration [8].