至于游戏玩家，英伟达或许要考虑重启旧产线（如RTX3060），移植一些新技术了。 西风 发自 凹非寺 量子位 | 公众号 QbitAI 黄仁勋最新发言： 机器人就是AI移民，能承担人类不愿意从事的工作。 现在内存卡了GPU的脖子。 在全球最大消费电子展CES 2026上，黄仁勋不仅一口气推出多款AI新品，在采访环节也是直面行业焦点。 而且老黄很忙，采访完下一站就是出席联想Tech World活动，与杨元庆 共同宣布" 联想人工智能云超级工厂 " ，英伟达最新发布的Vera Rubin将是该合作的重要组成部分 。 总之，老黄在自家发布会没说的内容，我们都整理在这了： 采访整理 CES 2026上，老黄整个核心主题都围绕着物理AI展开，包括机器人、自动驾驶等。 在机器人方面，他表示，从人口结构来看，我们已无法支撑起理想中的经济规模。因此， 需要更多的 AI新移民 ，来助力我们的生产车间 ， 承担那 些我们或许已不愿再从事的工作 。 紧接着又现身IEEE颁奖现场， 领取IEEE最高荣誉——2026年IEEE荣誉奖章 （2 026 IEEE Me dal of Honor） 。 IEEE授予黄仁勋该奖，以表彰其领先行 ...

点击下方 卡片 ，关注" 具身智能之心 "公众号 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。 想用3D高斯泼溅 （3DGS） 重建一座城市？ 过去，这往往意味着一套昂贵的GPU集群。如今，研究人员给出了另一种答案： 一张RTX 4090，加上足够大的CPU内存，也可以完成城市 级3D重建 。 来自纽约大学的研究团队在ASPLOS 2026上提出了名为 CLM （CPU-offloaded Large-scale 3DGS training） 的系统。该工作通过将3D 高斯泼溅训练中占用显存最多的参数转移到CPU内存中，使单张消费级显卡也能训练上亿规模的高斯点模型，为大场景神经渲染显著降低了 硬件门槛。 3DGS的规模应用瓶颈 3D高斯泼溅 （3DGS） 因其高质量渲染效果和极高的渲染速度，已成为神经渲染领域的重要技术路线。然而，当研究人员尝试将其用于城市 街区、大型室内空间等复杂场景时，问题很快显现出来—— GPU显存成为最直接、也最难解决的瓶颈 。 一个高精度的3DGS模型通常包含数千万乃至上亿个高斯点。每个高斯点包含位置、形状、颜色和不透 ...

3DGS的规模应用瓶颈 想用3D高斯泼溅（3DGS）重建一座城市？ 过去，这往往意味着一套昂贵的GPU集群。如今，研究人员给出了另一种答案：一张RTX 4090，加上足够大的CPU内存，也可以完成城市级3D重建。 来自纽约大学的研究团队在ASPLOS 2026上提出了名为 CLM（CPU-offloaded Large-scale 3DGS training）的系统。该工作通过将3D高斯泼溅训练中占用显 存最多的参数转移到CPU内存中，使单张消费级显卡也能训练上亿规模的高斯点模型，为大场景神经渲染显著降低了硬件门槛。 3D高斯泼溅（3DGS）因其高质量渲染效果和极高的渲染速度，已成为神经渲染领域的重要技术路线。然而，当研究人员尝试将其用于城市街区、大型室 内空间等复杂场景时，问题很快显现出来——GPU显存成为最直接、也最难解决的瓶颈。 一个高精度的3DGS模型通常包含数千万乃至上亿个高斯点。每个高斯点包含位置、形状、颜色和不透明度等数十个可学习参数，训练过程中还需同时保 存梯度和优化器状态。研究人员指出，即便是RTX 4090这样的24GB显存显卡，也只能容纳约一两千万个高斯点的完整训练状态，远不足以覆盖城市 ...

想用3D高斯泼溅 （3DGS） 重建一座城市？ 过去，这往往意味着一套昂贵的GPU集群。如今，研究人员给出了另一种答案： 一张RTX 4090，加上足够大的CPU内存，也可以完成城市 级3D重建 。 来自纽约大学的研究团队在ASPLOS 2026上提出了名为 CLM （CPU-offloaded Large-scale 3DGS training） 的系统。该工作通过将3D 高斯泼溅训练中占用显存最多的参数转移到CPU内存中，使单张消费级显卡也能训练上亿规模的高斯点模型，为大场景神经渲染显著降低了 硬件门槛。 3DGS的规模应用瓶颈 3D高斯泼溅 （3DGS） 因其高质量渲染效果和极高的渲染速度，已成为神经渲染领域的重要技术路线。然而，当研究人员尝试将其用于城市 街区、大型室内空间等复杂场景时，问题很快显现出来—— GPU显存成为最直接、也最难解决的瓶颈 。 非羊 整理自 凹非寺 量子位 | 公众号 QbitAI 一个高精度的3DGS模型通常包含数千万乃至上亿个高斯点。每个高斯点包含位置、形状、颜色和不透明度等数十个可学习参数，训练过程中 还需同时保存梯度和优化器状态。研究人员指出，即便是RTX 4090这样 ...

Core Viewpoint - Neural Rendering (NERF/3DGS) is revolutionizing 3D reconstruction technology, significantly enhancing the realism of images used in autonomous driving and embodied intelligence simulations, addressing the limitations of traditional computer graphics rendering [3][4]. Group 1: Background and Technology - NERF and 3DGS utilize neural networks to express spatial data, excelling in new perspective synthesis, which is crucial for sensor simulation in autonomous driving and embodied intelligence [3]. - The integration of NERF and 3DGS into existing simulation frameworks is proposed as a more efficient approach than developing new frameworks from scratch, allowing for real-time rendering while leveraging existing 3D digital assets and algorithm interfaces [3][4]. Group 2: Implementation in Simulation Software - NVIDIA's Isaac Sim has incorporated neural rendering technology, enabling the insertion of 3DGS models into simulation environments, allowing for both static backgrounds and dynamic interactive objects [4][5]. - The process of importing 3DGS models into Isaac Sim involves generating USDZ models and ensuring they possess physical properties for interaction within the simulation [5][8]. Group 3: Model Interaction and Physics - To achieve realistic interactions, imported models must have physical attributes added, such as collision properties, to ensure they interact correctly with other objects in the simulation [8][14]. - The integration of dynamic objects, such as a LEGO bulldozer, into the simulation environment demonstrates the capability of 3DGS models to interact with both static and dynamic elements [11][15]. Group 4: Performance and Future Considerations - The performance metrics indicate that even with a high workload, the simulation maintains a good frame rate and low memory usage, showcasing the efficiency of the neural rendering technology [17]. - Future challenges include improving light and shadow interactions between 3DGS models, providing accurate ground truth information for algorithms, and enhancing computational efficiency for larger scenes [18][19].

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].