Group 1 - Huawei officially launched the Mate 80 series smartphones, which support 3D facial recognition across the entire series [1] - The Mate 80 series features 3D ToF technology, ensuring financial-grade payment security and supporting over 150 mainstream applications for 3D facial login or payment [1] - Dongwu Securities predicts that 2024 will be the year of explosion for the 3D visual industry, with expanding application scenarios and increasing demand for high-precision perception and autonomous operation [1] Group 2 - Orbbec has applied its 3D visual sensors in various payment scenarios, including offline retail, self-service kiosks, dining, healthcare, and transportation [2] - OFILM leverages its optical technology and automated manufacturing capabilities to expand into new fields such as smart locks, VR/AR, machine vision, and action cameras [2]

以下文章来源于3D视觉之心 ，作者3D视觉之心 3D视觉之心 . 3D视觉与具身智能、机器人相关内容分享。 最强性价比3D激光扫描仪 GeoScan S1是国内目前最强性价比实景三维激光扫描仪，轻量化设计，一键启动，便可拥有高效实用的三维解决 方案。以多模态传感器融合算法为核心，实现厘米级精度的三维场景实时重构。可广泛用于多种作业领域。 每秒20万级点云成图，70米测量距离，360°全域覆盖，支持20万平米以上的大场景，扫描可选配3D高斯数据采 集模块，实现高保真实景还原。支持跨平台集成，配备高带宽网口及双USB 3.0接口，为科研实验提供灵活扩展 空间。降低开发门槛，助力开发者快速掌握研发能力，开启更多可能。 GeoScan S1设备自带手持Ubuntu系统和多种传感器设备，手柄集成了电源，可通过D-TAP转XT30母头输出至 GeoScan S1设备本体，给雷达、摄像头以及主控板提供电源。 欢迎联系小助手，进一步咨询细节 基础版重建效果一览！ 使用门槛低 ：操作简单直观，一键启动即可 执行扫描作业 扫描结果导出即用 ：无需复杂部署和繁琐处理，扫 描结果导出即用 高效率高精度建图 ：模型精度高，行走之间轻松 ...

编辑丨 机器之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 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! 现在，只需要一个简单的、用深度光线表示训练的 Transformer 就行了。 这项研究证明了，如今大多数 3D 视觉研究都存在过度设计的问题。 本周五，AI 社区最热门的话题是一篇新论文，有关 3D 建模的。 经过一年多的探索，来自字节跳动的团队推出了 Depth Anything 3（DA3），将单目深度估计扩展到了任何视角场景，让计算机实现了媲美人类的空间感知。 论文：https://arxiv.org/abs/2511.10647 项目页面：https://depth-anything-3.github.io 为了追求最小建模，DA3 的工作获得了 两个关键见解 ： 就是这样的方法， 在姿态估计方面比当前业界最先进的方法 (SOTA) 提升了 44%，在几何估计方面提升了 25%。 原来 3D 视觉竟然这么简单？ 纽约大学计算机科学助理教授、知名 AI 学者谢赛宁表示，论文有点像电影： ...

Core Insights - The article discusses the need for a "universal multimodal" 3D model, highlighting the limitations of current models that primarily rely on RGB images and fail to utilize additional geometric information effectively [5][6][9]. - The proposed OmniVGGT framework allows for flexible integration of any number of auxiliary geometric modalities during training and inference, significantly improving performance across various 3D tasks [6][9][10]. Group 1: Need for Universal Multimodal 3D Models - Current mainstream 3D models, such as VGGT, can only process RGB images and do not utilize depth or camera parameters, leading to inefficiencies in real-world applications [5]. - OmniVGGT addresses the issue of "information waste" and poor adaptability by fully leveraging available auxiliary information without compromising performance when only RGB input is used [9][10]. Group 2: Core Innovations of OmniVGGT - OmniVGGT achieves top-tier performance in tasks like monocular/multi-view depth estimation and camera pose estimation, even outperforming existing methods with just RGB input [7][29]. - The framework integrates into visual-language-action (VLA) models, significantly enhancing robotic operation tasks [7][29]. Group 3: Technical Components - The GeoAdapter component injects geometric information (depth, camera parameters) into the base model without disrupting the original feature space, maintaining low computational overhead [10][16]. - A random multimodal fusion strategy is employed during training to ensure the model learns robust spatial representations and does not overly depend on auxiliary information [22][23]. Group 4: Experimental Results - OmniVGGT was trained on 19 public datasets, demonstrating superior performance across multiple 3D tasks, with significant improvements in metrics such as absolute relative error and accuracy [29][30]. - The framework shows that the more auxiliary information is provided, the better the performance, with notable enhancements in depth estimation and camera pose accuracy [30][34]. Group 5: Practical Implications - OmniVGGT's design allows for flexible input combinations of auxiliary geometric modalities, making it practical for various applications in 3D modeling and robotics [53][54]. - The model's efficiency and speed, requiring only 0.2 seconds for inference, position it as a leading solution in the field [42][40].

机器之心报道 编辑：泽南、杨文 现在，只需要一个简单的、用深度光线表示训练的 Transformer 就行了。 这项研究证明了，如今大多数 3D 视觉研究都存在过度设计的问题。 本周五，AI 社区最热门的话题是一篇新论文，有关 3D 建模的。 经过一年多的探索，来自字节跳动的团队推出了 Depth Anything 3（DA3），将单目深度估计扩展到了任何 视角场景，让计算机实现了媲美人类的空间感知。 为了追求最小建模，DA3 的工作获得了 两个关键见解 ： 就是这样的方法， 在姿态估计方面比当前业界最先进的方法 (SOTA) 提升了 44%，在几何估计方面提升了 25%。 原来 3D 视觉竟然这么简单？ 纽约大学计算机科学助理教授、知名 AI 学者谢赛宁表示，论文有点像电影：第一部通常是最好的，续集往 往更复杂却并不更精彩。但这完全不适用于 DepthAnything 系列。 Bingyikang 的团队每次都能让事情变得更 简单、更易于扩展。 论文：https://arxiv.org/abs/2511.10647 项目页面：https://depth-anything-3.github.io 代码：htt ...

Core Insights - The company reported strong performance in Q3 2025, with total revenue of 714 million yuan, a year-on-year increase of 103.5%, and a net profit attributable to shareholders of 108 million yuan [1] - The company has achieved significant cost control, with a decrease in expense ratios across various categories, leading to improved profitability [1][2] - The company is positioned as a leader in the 3D vision market, with substantial global market share and strategic partnerships enhancing its growth prospects [2][3] Financial Performance - For the first three quarters of 2025, the company achieved total revenue of 714 million yuan, a 103.5% increase year-on-year, and a net profit of 108 million yuan [1] - In Q3 alone, the company generated revenue of 279 million yuan, up 102.49% year-on-year, with a net profit of 48 million yuan [1] - The company’s expense ratio for the first three quarters was 36.08%, down 35.18 percentage points year-on-year, indicating effective cost management [1] Profitability Metrics - The gross profit margin for the first three quarters was 42.80%, a decrease of 1.19 percentage points year-on-year, while the net profit margin increased by 32% to 15.08% [1] - The company’s R&D expense ratio was 20.52%, down 23.18 percentage points year-on-year, reflecting improved R&D efficiency [1] Market Position and Strategic Initiatives - The company holds a 72% market share in the 3D vision market for commercial and industrial mobile robots in South Korea, significantly reducing logistics costs for local enterprises [2] - Strategic partnerships have been established with leading robotics companies in Japan and collaborations in the humanoid robot sector, enhancing the company's technological capabilities [2] - The company has launched flagship 3D scanners and joined the Intel Partner Alliance, expanding its global developer ecosystem [2] Future Outlook - The company has adjusted its profit forecasts upward, expecting revenues of 936 million yuan, 1.476 billion yuan, and 1.898 billion yuan for 2025E, 2026E, and 2027E respectively, with year-on-year growth rates of 65.9%, 57.6%, and 28.6% [2] - Net profits are projected to reach 148 million yuan, 326 million yuan, and 467 million yuan for the same years, with year-on-year growth rates of 335.0%, 120.4%, and 43.4% [2][3]

Core Viewpoint - Tianzhun Technology is recognized as one of the leading companies in the domestic 3D vision sector, with a focus on applying 3D vision technology for quality control in industrial components [1] Company Summary - Tianzhun Technology actively engages with investors, providing insights into its position in the 3D vision market [1] - The company emphasizes its extensive application of 3D vision technology, particularly in the context of quality control for industrial parts [1] Industry Summary - The 3D vision technology sector is experiencing growth, with companies like Tianzhun Technology at the forefront, indicating a trend towards enhanced quality control measures in manufacturing [1]

Group 1 - The company is recognized as one of the leading enterprises in the domestic 3D vision sector [2] - The company widely applies 3D vision technology in quality control of industrial components [2]

Group 1 - The company, Tianzhun Technology, is recognized as one of the leading enterprises in the domestic 3D vision sector [1] - The application of 3D vision technology by the company is extensive, particularly in the quality control of industrial components [1]

Core Viewpoint - The automatic driving industry is rapidly evolving, with increasing demand and a growing number of business lines, indicating a healthy development in the sector [1]. Group 1: Team Overview - The team has developed four key IPs over two years: embodied intelligence, automatic driving, 3D vision, and large model technology, with a total audience of nearly 360,000 across various platforms [1]. Group 2: Recruitment - The company is hiring full-time and part-time positions for operations and sales roles to support its expanding business [2]. Group 3: Job Responsibilities and Requirements - Responsibilities for the operations role include managing course progress, enhancing platform engagement, and developing content related to the automatic driving and AI industries [4]. - The sales role involves creating promotional content for online and hardware products and liaising with hardware manufacturers and academic/enterprise clients [5][6]. - Candidates are expected to have strong execution skills, a relevant educational background, and familiarity with social media platforms [12]. Group 4: Growth Opportunities - The company offers exposure to top-tier operational teams, providing opportunities to learn operational techniques and sales strategies, which can lead to rapid personal growth [7]. - There are also opportunities for further academic pursuits, such as research and doctoral studies, which can enhance personal development [9].