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“六小龙”之群核科技扭亏背后:既要扩张又要节流
Bei Jing Shang Bao· 2025-08-27 14:39
首份招股书失效后,群核科技近日向港交所递交更新后的招股书。2025年上半年营收3.99亿元,同比增长9%,经调整净利 润转正,但销售及营销开支、研发开支较上半年同期分别缩水20.7%和16.8%;截至2025年6月底,赎回负债40亿元。 严重依赖订阅业务的营收结构也未改变。2025年上半年,群核科技来自企业及个人客户的软件订阅营收占比97.7%,向企 业客户提供的专业服务营收占比2.3%。2024年推出的空间智能解决方案SpatialVerse,客户数10个。 根据群核科技的计划,上市募集的资金将用于国际扩张策略、推出新产品等。招股书更新后,这家首个公开上市计划的杭 州"六小龙"企业,紧接着发布两款空间开源模型。在研发、销售开支递减,赎回负债高悬的当下,群核科技的盈利能否持 续,还需要时间解答。 专业服务营收占比2.3% 抛开营收结构,群核科技2025年上半年最大的变化是扭亏为盈,公司经调整净利润1782.5万元,上年同期该数字是经调整 净亏损7319.6万元。2022—2024年,公司也处于亏损状态,经调整净亏损分别是3.38亿元、2.42亿元、7004.9万元。 这份扭亏的招股书公开不久,群核科技发布两款 ...
群核科技港股IPO:20亿融资大部分已“烧光” 近40亿元赎回负债悬顶 左手裁员降本右手“画饼”大举扩张
Xin Lang Zheng Quan· 2025-08-26 02:21
8月22日,群核科技(Manycore)更新招股书,二次向联交所递交上市申请,由摩根大通、建银国际担任联席保荐人。 据招股书披露,群核科技拟将募集资金用于:实施公司的国际扩张策略;增强现有产品的功能,并推出新产品及/或功能,以满足现实世界空间及虚拟环境 的需求;支持公司的国内销售及市场推广活动,并提升公司的品牌知名度;投资公司的核心技术及基础设施;营运资金及一般企业用途。 不难看出,群核科技上市募资的主要目的是推动业务全球化扩张。但矛盾的是,近年来,群核科技大力推进裁员节流,研发费用率从2022年的72.9%锐减至 2024年的44.7%,并进一步降至2025年上半年的37.5%,同期销售费用率也从53.4%下滑至33.9%。 2025年上半年,群核科技经调整净利润终于实现扭亏为盈,不过经营现金流量净额依旧为负,2022年至今累计净流出5.49亿元。更为严峻的是,自2021年10 月的E+轮融资后,群核科技再未获得新的融资,资本市场似乎已对其失去兴趣。截至2025年6月底,赎回负债已接近40亿元。 出品:新浪财经上市公司研究院 作者:君 上市融资,对群核科技而言不是锦上添花,而是救命稻草。 接近40亿赎回负债 ...
群核科技发布3D高斯语义数据集,给机器人装上“空间大脑”
具身智能之心· 2025-07-26 10:45
Core Viewpoint - The release of the InteriorGS dataset by Qunhe Technology aims to enhance spatial perception capabilities for robots and AI agents, marking a significant advancement in the field of AI training [2][5]. Group 1: InteriorGS Dataset - The InteriorGS dataset includes 1,000 3D Gaussian semantic scenes covering over 80 types of indoor environments, providing AI agents with a "spatial brain" to improve their environmental understanding and interaction capabilities [2][5]. - This dataset is claimed to be the world's first large-scale 3D dataset suitable for the free movement of intelligent agents [2][5]. Group 2: Technological Advancements - Qunhe Technology has successfully applied 3D Gaussian technology in various fields, including cultural heritage preservation and spatial design, with notable projects such as the digital restoration of a 60-year-old photo studio in Hangzhou [4][6]. - The InteriorGS dataset leverages the efficiency and cost advantages of 3D Gaussian technology in scene reconstruction, combined with the company's self-developed spatial large model capabilities, resulting in a dataset that balances realism and semantic understanding [5][6]. Group 3: Industry Impact and Collaboration - Qunhe Technology's SpatialVerse platform has accumulated a vast amount of interactive 3D data and a set of physical simulation tools, aiming to become the "ImageNet" of the spatial intelligence field, similar to how ImageNet propelled the explosion of computer vision [7]. - The company has formed partnerships with several embodied intelligence firms, including Zhiyuan Robotics and Galaxy General, indicating its growing influence in the industry [7]. Group 4: Future Directions - The company emphasizes the importance of the Sim2Real paradigm as the most efficient training method for embodied intelligence, aiming to promote a "real-virtual-real" framework in collaboration with industry players [8].
IPO观察|群核科技:期内亏损近18亿元,资产负债率754%,业绩压力大
Sou Hu Cai Jing· 2025-07-14 05:31
Core Viewpoint - The company, Qunhe Technology, is facing significant challenges as it attempts to go public in Hong Kong, with ongoing losses, high debt levels, and a declining customer loyalty impacting its growth prospects [1][16]. Group 1: Company Overview - Qunhe Technology, established in 2011, is a leading space design platform leveraging AI technology and GPU clusters, with products including "CoolJia" and "Coohom" [2][5]. - The company submitted its IPO application in February 2023, but the China Securities Regulatory Commission (CSRC) has requested additional data regarding compliance issues, particularly concerning data security and corporate governance [2][4]. Group 2: Financial Performance - Qunhe Technology reported revenues of RMB 600.6 million, RMB 663.5 million, and RMB 552.9 million for the years 2022, 2023, and the first nine months of 2024, respectively, with a year-on-year growth of 10.48% in 2023 [7][10]. - Despite being the largest space design software provider with a market share of 22.2%, the company has struggled with profitability, posting net losses of RMB 703.7 million, RMB 646.1 million, and RMB 489.5 million during the same periods [8][10]. Group 3: Cost Structure - The company has a high cost structure, with R&D expenses accounting for 72.9%, 58.9%, and 47.6% of revenue in the respective years, while sales and marketing expenses also remain significant [10][11]. - The operating loss has been substantial, with operating losses of RMB 402.1 million, RMB 294.0 million, and RMB 128.4 million reported for the same periods [8][10]. Group 4: Customer Dynamics - Over 90% of Qunhe Technology's revenue comes from subscription fees and technical service fees, heavily reliant on enterprise clients [11][12]. - The number of enterprise clients increased from 33,058 in 2022 to 41,070 in 2023, but the average subscription revenue per client has decreased, indicating a decline in customer loyalty [12][13]. Group 5: Debt and Liquidity - The company's debt levels are concerning, with a debt-to-asset ratio of approximately 754.4% as of September 30, 2024, and total liabilities increasing significantly [14][15]. - Cash flow issues are evident, with operating cash flow decreasing to RMB 165 million and cash and cash equivalents dropping by 45.2% [15][16].
具身智能数据:AI时代的石油
Soochow Securities· 2025-06-05 01:23
Investment Rating - The industry investment rating is "Overweight" indicating an expected outperformance of the industry index relative to the benchmark by more than 5% in the next six months [81]. Core Insights - Data is the key driver for the rapid breakthroughs and practical applications of embodied intelligence technology, similar to the path of autonomous vehicles. High-quality datasets are essential for training and deploying intelligent agents to effectively complete complex tasks [3][17]. - There is a current scarcity of high-quality and diverse datasets for embodied intelligence, which is crucial for the training of robots. The need for standardized and validated datasets is a pressing requirement in the industry [3][17]. - The report emphasizes the importance of both real and simulated data for training embodied intelligence models, highlighting their complementary roles in the data collection process [22][24]. Summary by Sections 1. Basic Concepts of Embodied Intelligence Datasets - Embodied intelligence datasets are categorized into real data and simulated data, with real data collected through physical interactions and simulated data generated in virtual environments [22][24]. 2. Current Status of Domestic and International Real Datasets - Various high-quality embodied intelligence datasets have been released, such as AgiBot World and Open X-Embodiment, showcasing a wide range of tasks and skills [30][31]. 3. Current Status of Domestic and International Simulated Datasets - The report discusses the technological pathways for scene generation and simulation in creating simulated datasets, emphasizing the importance of both methods in training [50][51]. 4. Related Companies - Key companies to watch in the embodied intelligence data sector include Junsheng Electronics, Haitian Ruisheng, Suochen Technology, and Huaru Technology, which are involved in data collection and simulation [76].
具身空间数据技术的路线之争:合成重建VS全端生成
量子位· 2025-04-20 13:24
Core Viewpoint - The breakthrough in embodied intelligence relies heavily on high-quality data, with a significant focus on synthetic data generation due to the high costs of real data collection [1][2]. Group 1: Data Challenges - The current state of embodied intelligence data is characterized by scarcity and inadequacy, with existing sources being limited and not sufficiently diverse [16][18]. - Three main categories of existing data sources are identified: real scan data, game engine environments, and open-source synthetic datasets, each with its limitations [17]. - The indoor embodied intelligence scenarios require structured, semantic, and interactive 3D scene data, which is challenging to collect due to the unique layouts and usage patterns of individual households [18][19]. Group 2: Technical Approaches - There are two primary technical routes for synthetic data generation: "video synthesis + 3D reconstruction" and "end-to-end 3D generation" [3][24]. - The "video synthesis + 3D reconstruction" approach involves generating video or images first, which can lead to cumulative errors and limited structural accuracy [24][39]. - The "end-to-end 3D generation" method aims for direct synthesis of structured spatial data but faces challenges such as low generation quality and lack of common sense [67][68]. Group 3: Innovations in Data Generation - A new technical solution called "modal encoding" is proposed to address the common sense gap in end-to-end 3D generation, allowing for the digital encoding and implicit learning of spatial solutions [5][91]. - The Sengine SimHub is introduced as a system that integrates design knowledge into the generation process, enhancing the stability and adaptability of the generated data [75][78]. - The focus is on creating a data generation system that not only produces space but also generates "understandable and usable" environments, incorporating design logic and user preferences [91][96]. Group 4: Future Directions - The industry is at a critical juncture where the need for a new approach to data generation is evident, moving beyond mere data accumulation to creating "useful data" [95][96]. - The future of embodied intelligence may hinge on how space is defined and understood, emphasizing the importance of integrating rules and preferences into spatial data generation [96][100].
深度|具身合成数据的路线之争,谁将率先走出困境?
Z Potentials· 2025-04-08 12:30
Core Viewpoint - The article discusses the competition between two main technical routes for embodied synthetic data: "Video Synthesis + 3D Reconstruction" and "End-to-End 3D Generation" [1][49]. Group 1: Challenges in Embodied Intelligence - The development of robots has seen faster advancements in physical capabilities compared to cognitive abilities, leading to difficulties in unfamiliar environments [3]. - Embodied intelligence requires an integrated ability of perception, reasoning, and decision-making, which is contingent on a clear understanding of spatial structures [4]. - Current AI advancements are hindered by a lack of high-quality spatial data, which is essential for effective cognitive functioning [5]. Group 2: Data Dilemma - The existing data for embodied intelligence is limited and insufficient, categorized into three types: real scanned data, game engine environments, and open-source synthetic datasets, all of which have significant limitations [6]. - The unique layout and usage patterns of homes create challenges in collecting comprehensive training data, making traditional data collection methods impractical [8]. Group 3: Technical Routes - The two main technical paths for synthetic data generation are: 1. Video Synthesis + 3D Reconstruction: This method generates video or images first, then reconstructs them into 3D data, facing issues with accuracy and physical consistency [11][13]. 2. End-to-End 3D Generation: This approach directly synthesizes structured spatial data using advanced techniques like Graph Neural Networks (GNNs) and diffusion models, but struggles with generating high-quality outputs [22][39]. Group 4: Innovations in 3D Generation - New methods such as "modal encoding" aim to integrate design knowledge into the generation process, enhancing the model's ability to create reasonable spatial structures [2][44]. - The Sengine SimHub framework incorporates training processes that improve the stability and adaptability of the generated data, aligning it more closely with real-world logic and semantics [45][48]. Group 5: Future Directions - The industry faces a "data drought" compared to the more established data loops in autonomous driving, necessitating innovative approaches to spatial understanding and generation [49]. - The future of embodied intelligence may hinge on how spatial concepts are defined and understood, emphasizing the need for a system that embeds rules and preferences into spatial data generation [50].
群核科技亮相GTC,创始人黄晓煌回应卖英伟达股票创业:光谈钱就没意思了
IPO早知道· 2025-03-21 11:52
这是一个基于大语言模型的3D场景语义生成框架 ——其 突破了传统大语言模型对物理世界几何与 空间关系的理解局限,赋予机器类似人类的空间认知和解析能力。 这相当于为具身智能领域提供了 一个基础的空间理解训练框架,企业可以针对特定场景对SpatialLM模型微调,降低具身智能训练门 槛。 群核科技董事长黄晓煌 表示: "我们希望打造一个从空间认知理解到空间行动交互闭环的具身智能 训练平台。本次开源的SpatialLM空间理解模型旨在帮助具身智能机器人完成在空间认知理解上的基 础训练。而去年群核科技发布的空间智能解决方案SpatialVerse,则希望进一步通过合成数据方案 为机器人搭建最接近物理真实的'数字道场',实现机器人在仿真环境中的行动交互训练。" 从GPU高性能计算到具身智能训练。 本文为IPO早知道原创 作者|Stone Jin 微信公众号|ipozaozhidao 据 IPO 早 知 道 消 息 , 群 核 科 技 于 3 月 19 日 在 GTC2025 全 球 大 会 上 宣 布 开 源 空 间 理 解 模 型 SpatialLM。 在空间和具身智能训练上,目前群核科技已与硅谷头部科技企业等在内的 ...
IPO周报 | 蜜雪冰城通过港交所聆讯;群核科技冲刺「全球空间智能第一股」
IPO早知道· 2025-02-16 13:39
一周IPO动态,覆盖港股、美股、A股。 本文为IPO早知道原创 作者|C叔 微信公众号|ipozaozhidao 蜜雪冰城 港股|通过聆讯 据IPO早知道消息,蜜雪冰城股份有限公司(以下简称"蜜雪冰城")日前已通过港交所聆讯并于2月 14日晚间披露通过聆讯后的资料集。 这意味着,蜜雪冰城即将在港挂牌上市。 截至2024年12月31日,蜜雪冰城的门店数量为46479家。2024年,蜜雪冰城门店网络实现出杯量约 90亿杯,同比增长约21.9%;终端零售额约583亿,同比增长约21.7%。根据灼识咨询的报告,仅以 截至2024年9月30日的门店数量计算,蜜雪冰城已经成为全球第一的现制饮品企业。 在东南亚市场,蜜雪冰城也是排名第一的现制茶饮品牌。截至2024年9月30日,蜜雪冰城门店网络 已覆盖中国及印度尼西亚、越南、马来西亚、泰国等海外11个国家,门店数量约4,800家门店。 同时,蜜雪冰城的门店网络终端零售额、饮品出杯量同样持续增长——2021年、2022年、2023年 及2024年,门店终端零售额分别为228亿元、307亿元、478亿元及583亿元,饮品出杯量分别为36 亿杯、47亿杯、74亿杯及90亿杯。 以 ...
群核科技(酷家乐母公司)冲刺港交所:或将成为「全球空间智能第一股」
IPO早知道· 2025-02-14 12:14
通过空间智能技术构建了一套物理正确的世界模拟器,被称为"机器人的训练道场"。 本文为IPO早知道原创 作者|Stone Jin 微信公众号|ipozaozhidao 据IPO早知道消息,Manycore Tech Inc.(杭州群核信息技术有限公司的控股公司,以下简称"群核 科技")于2025年2月14日正式向港交所递交招股说明书,拟主板挂牌上市,摩根大通和建银国际担 任联席保荐人。 成立于2011年的群核科技作为一家以人工智能(AI)技术和专用图形处理单元(GPU)集群为底座的空 间智能企业,构建了一套物理正确的世界模拟器。在过去数年里,群核科技一直专注于空间认知相关 技术,2024年正式发布了基于三维空间的多模态CAD大模型,并通过逼真的虚拟模拟帮助训练复杂 模型。这些技术沉淀如今被广泛运用在室内空间场景下的实时渲染、工业生产制造,以及虚拟物理世 界训练等场景中。 这意味着,群核科技或将成为"全球空间智能第一股" 。 值得一提的是, 群核 科技 与DeepSeek 、 宇树科技、游戏科学等杭州新技术前沿领域企业共同被外界称之为"杭州六小龙" 。 群核科技的业务重点始于空间设计和可视化, 根据弗若斯特沙利文 ...