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想把沙发挪到另一面墙，又怕搬完才发现原来的位置更好。禾赛想让这一步先发生在电脑里。10月14日，这家以车载激光雷达闻名的公司发布了Kosmo三维世界数据引擎，核心是一台手持设备Kosmo One：它外形像大了一号的大疆Pocket，扫描一个房间，就能得到一个可以在虚拟空间里挪动家具的副本。
Want to move the sofa to the other wall but worry that you'll only realize the original spot was better after moving it? Hesai wants that step to happen on a computer first. On October 14, the company, known for its automotive lidar, unveiled the Kosmo 3D world data engine. Its core is a handheld device, the Kosmo One, which looks like a slightly bigger DJI Pocket. Scan a room with it, and you get a copy in which you can move furniture around in a virtual space.
原理并不复杂。激光雷达（一种用激光测距的传感器）负责测量物体的形状、距离和位置，一英寸CMOS影像系统（即摄像头的感光元件）记录颜色、纹理和光影，两部分信息再对齐，还原出真实空间。禾赛称其几何精度已推至毫米级，能清晰辨识网球拍上约5mm间距的网孔。影像部分最高支持8K采集，用户最终导出文件的最高分辨率为6K。与普通相机不同，它要尽量避免风格化处理，保留现场原有的颜色与光影，并通过为三维重建专门调教的ISP图像处理系统，减少不同摄像头、不同时刻之间的差异，让多个视角拼成连贯的场景。采集时，激光雷达、影像系统与高精度IMU（惯性测量单元，用来记录设备的位置和姿态）在微秒级上同步。据禾赛公布的数据，普通室内场景下，扫描约100平方米只需约15分钟。
The principle is not complicated. The lidar (a sensor that measures distance with lasers) measures the shape, distance and position of objects, while a one-inch CMOS imaging system (the light-sensing element of a camera) records color, texture and lighting. The two sets of information are then aligned to recreate the real space. Hesai says its geometric accuracy has reached millimeter level, clear enough to distinguish the mesh holes on a tennis racket spaced about 5mm apart. The imaging system supports capture at up to 8K, and the highest resolution of the files users can export is 6K. Unlike ordinary cameras, it avoids stylized processing as much as possible, preserving the original colors and lighting on site. A ISP image-processing system tuned specifically for 3D reconstruction reduces differences between cameras and between moments in time, so that multiple viewpoints can be stitched into a coherent scene. During capture, the lidar, the imaging system and a high-precision IMU (inertial measurement unit, which records the device's position and attitude) are synchronized at the microsecond level. According to Hesai's data, scanning about 100 square meters of an ordinary indoor space takes only about 15 minutes.
采集完成后，云平台Kosmo World接手。它借助AI语义分割识别场景中的物体，把它们提取为可以独立编辑的三维对象。发布会上，系统根据语言提示识别出沙发并将其分割出来，沙发移走后，AI根据周围信息补全了被遮挡的地毯纹理。禾赛还做了一组对比：同一房间拍摄20张照片后交给GPT-6 Astra重建，沙发位置和形状出现偏差，材质与光影也有偏差；把Kosmo采集的3DGS数据交给它，物体之间的位置关系有所改善，但椅子外形、花的材质等细节仍有偏差。禾赛的说法是，通用模型像“大脑”，Kosmo One负责提供更准确的“眼睛”。这些对比数据均来自禾赛自己的发布会演示，尚未见到独立测试。
Once capture is complete, the Kosmo World cloud platform takes over. It uses AI semantic segmentation to identify objects in a scene and extract them as 3D objects that can be edited independently. During the launch event, the system identified a sofa from a language prompt and separated it out. After the sofa was removed, the AI filled in the hidden carpet texture based on the surrounding information. Hesai also ran a comparison: 20 photos of the same room were fed to GPT-6 Astra for reconstruction, and the sofa's position and shape were off, as were its materials and lighting. When the 3DGS data captured by Kosmo was fed to the same model, the spatial relationships between objects improved, but details such as the shape of the chair and the material of the flowers were still off. Hesai's argument is that general-purpose models act as the “brain,” while the Kosmo One provides more accurate “eyes.” All of these comparisons come from Hesai's own launch demonstration, and no independent testing has been seen yet.
这项技术已经进入专业人士的工作流程。皮克斯旗下工业级渲染器RenderMan的创意负责人Dylan Sisson扫描了园区广场上的台灯和彩色小球雕塑，扫描结果可以在软件里调整位置、大小和透明度，反复用于不同制作环节。机器人公司Sharpa则用Kosmo还原了一家DQ门店，搭建机器人的训练环境，禾赛称其机器人在该环境中的任务成功率达到99%。据TechNode报道，这次扫描替代了约300小时的人工建模。
The technology has already entered professionals' workflows. Dylan Sisson, creative director of RenderMan, the industrial-grade renderer from Pixar, scanned a desk lamp and a sculpture of colored balls on a campus plaza. The scans can be adjusted in software for position, size and transparency, and reused across different stages of production. Robotics company Sharpa used Kosmo to recreate a DQ store and build a training environment for its robots. Hesai says its robots achieved a 99% task success rate in that environment. According to TechNode, this scanning replaced about 300 hours of manual modeling.
那么，对普通人意味着什么？眼下并不意味着马上能用。禾赛尚未公布Kosmo的零售售价，短期内，个人用户还无法用它扫描自己的房间。要走进日常生活，设备还需要更便携，从采集到调用的流程也要更省事。禾赛表示，Kosmo将继续迭代，采集设备也有机会通过合作伙伴，以手持之外的形态进入更多场景。对于开发者和专业制作团队来说，更直接的变化已经出现：一次扫描就能留下可以被反复调用的三维场景，既能用于影视，也能用来训练机器人。
So what does this mean for ordinary people? For now, it does not mean they can use it right away. Hesai has not announced a retail price for the Kosmo, and individual users still cannot use it to scan their own rooms in the short term. To become part of everyday life, the device would need to be more portable, and the process from capture to use would need to be simpler. Hesai says the Kosmo will keep being iterated, and its capture device may also reach more scenarios through partners in forms other than handheld. For developers and professional production teams, the more immediate change has already arrived: a single scan can produce a 3D scene that can be repeatedly reused, whether for film or for training robots.