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Ouster’s new color lidar is coming to replace cameras

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The tech industry has spent the last decade asking whether self-driving cars need lidar sensors, cameras, or all of the above. Lidar company Ouster says it has a new answer: put them both in the same sensor.

On Monday, the San Francisco-based company announced a new lineup of lidar sensors it calls “Rev8,” all of which offer so-called “native color lidar.” These sensors are capable of capturing color imagery and three-dimensional depth information at the same time, doing the work of two sensors in one.

Ouster CEO Angus Pacala said the development has been a decade in the making at his company, and he wasn’t shy about his ambitions for the new product lineup in an exclusive interview with TechCrunch, calling it the “holy grail of what a roboticist has always wanted.”

“For all of human history, it’s been: you buy a lidar sensor, you buy a camera, and you try to make sense of the combination with some higher level reasoning, and waste an enormous amount of time doing this,” he told TechCrunch. “And companies only get really halfway there in terms of calibrating and fusing the data streams.”

Ouster’s new sensors, he said, change this equation.

“The goal is to obviate cameras. There’s no reason that one sensor can’t do both,” he said.

The Rev8 lineup arrives at a dynamic moment for lidar companies. There has been a years-long wave of consolidation happening, with Ouster buying Velodyne, and Luminar’s assets recently getting acquired in bankruptcy.

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At the same time, the market for sensors is exploding. Waymo and others have finally deployed working robotaxis and are scaling quickly. Robotics companies — humanoid and industrial — are hoovering up investment dollars and need sensors to perceive the world. There’s so much interest in the space that new companies like Boston-based Teradar are popping up and testing the waters with entirely new modalities. (In Teradar’s case, it’s using terahertz imaging.)

A color lidar that combines pinpoint depth information with camera-quality image data could be especially valuable to the robotics players, Pacala said. And he said Ouster worked with Fujifilm and image science company DXOMARK to understand “what it means to build a great camera.”

In fact, Pacala claims Ouster’s color lidar is “improving in many ways on a modern camera” thanks to the way the company already designs and builds its sensors.

Ouster uses so-called “digital lidar” architecture. Instead of the analog approach, which involves many moving parts, Ouster captures the lidar info directly on its custom chip using what’s known as single photon avalanche diode (SPAD) detectors.

The company is using this same SPAD technology to capture the color image data in the Rev8 sensors. Pacala said this novel technique allows its image capture to be more sensitive than a normal camera.

“It’s 48-bit color, 116 dB of dynamic range, like mega pixel resolution. These are top line numbers that make it pound for pound good camera. But it just so happens it’s coming as a pre-fused data stream as a 3D colorized point cloud,” he said. “You can actually use the data as a camera stream as well, but it’s that’s one of the powers of this system, is you can use just the lidar data stream, you can use just the camera data stream, or you can use the pre-fused data stream, depending on how kind of forward-thinking your perception team is.”

Pacala said his company has already shipped samples to existing customers and that it’s now taking orders. He said he’s particularly proud of the OS1 Max sensor, which he said he considers to be “the industry’s best long range lidar.” It can see 500 meters in all directions and is smaller than other long range lidar “by a big margin.”

“We’ve had a long range LiDAR, but it hasn’t been just like clearly a cut above everything else,” he said. “That’s a big leap for Ouster. I think it means that we’ll start to see it much more on high-speed robo-trucking, robotaxi applications, I think a lot of drone stuff will transition to the OS1 Max.”

Other new lidars built on the Rev8 platform will include the OS0, OS1, and OSDome, according to a press release.

Ouster isn’t the only company that has started talking about color lidar. Last month, Chinese company Hesai announced its own color lidar platform that it says will enter mass production by the end of this year. Other companies, like Innoviz, have previously pitched their own takes on “color lidar.”

Pacala says most other players trying to “fuse” cameras and lidar sensors are basically packaging them together in a box, though. The approach Ouster (and, to be fair, Hesai) is taking is putting the lidar and imaging tech on the same chip.

This dramatically cuts down on the amount of work Ouster’s customers have to do to make sense of the competing sensor streams, Pacala said, and it also sets those customers up to eventually eschew cameras altogether — all while being cheaper and smaller than Ouster’s previous technology.

“This is kind of fundamentally changing the value proposition of what we’re selling to a customer from this stage forward,” he told TechCrunch.

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AMD will acquire Fei-Fei Li’s World Labs for $8.2 billion

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World Labs, one of the leading developers of deep learning models intended to understand physical reality, has been acquired in a $8.2 billion deal, the two companies said today.

World Labs justified the deal in a statement saying that AI development required “close collaboration across model reseach, systems and compute.” AMD, in turn, says that understanding frontier workloads like those created at World Labs will shape its chip-making roadmap.

The acquisition will see World Labs founder Fei-Fei Li join AMD as executive vice president and chief scientist. The two companies formed an inference optimization and training partnership last year, and ties have remained close. Notably, Li was a guest at AMD’s CES presentation earlier this year.

Li, a Stanford computer science professor, is considered a pioneer of AI, particularly computer vision, for her role pioneering the ImageNet database and subsequent challenges. In 2024, Li founded World Labs to develop deep learning models with a more robust understanding of reality, arguing that true general intelligence required a grounding in physics and the ability to understand and reason about data beyond text.

In a post announcing the deal, Li described the partnership as the result of a desire to scale World Labs’ technical breakthroughs beyond the lab. “Now that we have tangible proof of the possibilities, we want to do everything we can to accelerate the future,” Li wrote in the post. “To do this requires scaling our efforts, widening our reach, and getting closer to the hardware.”

“World model” remains a loose term, encompassing everything from language models trained to understand visual inputs, to models capable of generating and sustaining a high-fidelity simulation of reality. World Labs’ first product, Marble, is pitched for creating entertainment experiences, but also for the ability to create simulated environments for robot training.

The acquisition is likely to help AMD compete with long-standing rival Nvidia in creating an ecosystem for AI-specific chips. While Nvidia already has a suite of open-weight world models like Cosmos, AMD has only offered text- and video-based models to the public.

World models are seen as vital in efforts to deploy generative AI models on robotic platforms, from autonomous vehicles to industrial robots and general-purpose humanoids. In particular, the dearth of useful data to train general purpose robots means that synthetic data from world models will be key to realizing the vision put forward by companies like Tesla and Figure.

The acqusition is expected to close before the end of the year, subject to regulatory approval.

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Nvidia Adds Hardware Monitoring Design to AI Agent Safety Platform

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Nvidia adds a Sentry hardware monitoring design to its AI agent safety platform. See what OpenShell offers now and what IT teams still need to verify.

The post Nvidia Adds Hardware Monitoring Design to AI Agent Safety Platform appeared first on TechRepublic.

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Shopify opens checkout to browser-based AI agents

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While some retailers, like Amazon (and Adidas, apparently!), are blocking AI agents from making purchases on users’ behalf on their respective platforms, e-commerce platform Shopify has moved in the other direction.

On Monday, the company announced that browser-based AI agents can now complete purchases on Shopify merchants’ sites, extending their capabilities beyond just searching for products and adding items to carts.

Shopify previously supported WebMCP for its storefronts and carts, allowing browser-based AI agents to comb through a Shopify retailer’s inventory, search for products, and add them to a cart. The addition of WebMCP support for checkout, including Shop Pay, means these agents can now read the checkout screen, update it, and submit the transaction with the buyer’s authorization, without relying on screenshots or scraping webpages, the company said.

Image Credits:Shopify

This update introduces three new tools — get_checkout, update_checkout, and complete_checkout — that allow agents to inspect a checkout, change things like the customer’s address or delivery option, and then place an order after the buyer authorizes it.

The feature is rolling out to all eligible Shopify merchants, said Gil Greenberg, a staff product manager who works on agentic commerce at Shopify, in a post on X.

Shopify already offers a hosted Model Context Protocol (MCP) server, which allows agents to work server-to-server. The proposed standard WebMCP, meanwhile, is designed for agents that work inside the buyer’s browser. Both leverage Shopify’s Universal Commerce Protocol (UCP), which provides a common way to search for and discover products, build carts, and check out.

Top AI agents like Muse and Instinct already have direct partnerships with Shopify for agentic commerce. The Instinct partnership was announced today.

“If your agent is operating in the buyer’s browser, use WebMCP tools provided on storefront and checkout to efficiently complete order placement, instead of navigating HTML built for humans,” Greenberg wrote on X. “These WebMCP tools provide structured and efficient APIs, purposely designed — via UCP — to ensure accurate commerce facts, required disclosures, and handoff requirements.”

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