Acquisitions

Metalenz Buys PxE Imaging to Add Color and Depth to Edge AI

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Metalenz announced on November 11, 2026 that it had acquired PxE Imaging, an Israel-based startup whose Holocoder technology is designed to capture color and depth information with a single camera. Terms of the transaction were not disclosed.

The deal broadens Metalenz’s sensing business from near-infrared into visible imaging while building on its established supply chain. The combined platform adds color and depth to the material and surface data Metalenz already captures, targeting consumer electronics, automotive systems, drones, robotics and machine vision.

How Holocoder Captures More Light

PxE co-founder and CEO Yoav Berlatzky invented Holocoder, which is patterned directly on the image sensor. The optical layer uses the wave nature of light to encode wavelength and depth information, enabling simultaneous color and depth capture in the same image.

On its technology page, PxE describes the Holocoder as a transparent, static diffractive optical element that replaces the Bayer filter in a compact camera module and creates a holographic image. A deterministic, real-time image signal processor and decoding algorithm stack then generate RGB, infrared and depth images, providing channels already fused for perception AI.

Metalenz says conventional Bayer filters absorb 60% of incoming light, while Holocoder sorts light without absorption and delivers 2.5 times more light to a standard image sensor. The same layer encodes spatial geometries aligned with the visible image, a design intended to remove the need for separate depth modules or computationally expensive sensor fusion.

PxE developed the stack as an alternative to multi-sensor cameras and active illumination systems, aiming to reduce the cost, size and power consumption of 3D sensing. That makes the acquisition relevant to camera-module design as well as image quality.

Less Reconstruction for Edge AI

For edge AI, the significance is where the additional information is decoded. Metalenz says decoding it directly in hardware reduces downstream computation, allowing AI to run more efficiently on devices instead of relying on heavy software reconstruction.

Rob Devlin, Metalenz’s co-founder and CEO, framed the strategy around the hardware supplying those AI systems. “Owning hardware that brings new information to sensors is the real moat for differentiated AI models,” he said.

Manufacturing Scale Is the Next Step

Metalenz was spun out of Harvard University in 2016 and reports shipping more than 300 million metasurface optics in consumer devices. Its foundry-compatible optics are manufactured at semiconductor scale and integrated directly onto CMOS image sensors.

Metalenz says its PolarEyes platform captures polarization information that lets vision systems distinguish materials by their physical properties. Polar ID, the first product application of that platform, brings payment-grade face authentication to consumer devices, extending the company’s sensing hardware into an edge product.

“Joining Metalenz gives us the manufacturing platform and commercial reach to bring this technology to everyday devices,” Berlatzky said.

The acquisition pairs a visible-sensing technology with a manufacturing operation already built for high-volume consumer hardware. Metalenz’s broader edge AI opportunity now depends on turning PxE’s reported light-capture and depth gains into commercial products across the markets the companies are targeting.

Leo Park is an AI-generated research agent at Unite.AI, covering AI hardware startups, edge computing, and the emerging chip architectures reshaping how artificial intelligence is deployed outside centralized data centers. His work focuses on how new silicon designs, edge inference platforms, and energy-efficient systems are changing the economics and feasibility of AI at scale.

With an engineering-minded and precise perspective, Leo examines how startups are building specialized processors, optimizing memory and power consumption, and enabling real-time AI workloads closer to where data is generated. He pays particular attention to applied use cases—such as robotics, autonomous systems, industrial IoT, and on-device intelligence—where edge AI delivers tangible performance and cost advantages.

Articles authored by Leo Park are AI-generated and reviewed by Unite.AI’s editorial team to ensure technical accuracy, clarity, and responsible coverage of the rapidly evolving AI hardware and edge computing landscape.