While traditionally not available in the public domain, access to the complete image signal processing (ISP) algorithmic pipeline and its hardware implementation are necessary to enable new imaging use cases and to improve the performance of high level deep learning vision networks. In this paper, we present Infinite-ISP: A complete hardware ISP development suite, comprising of an ISP algorithm development platform, a bit-accurate fixed-point reference model, an ISP register transfer level development platform, an FPGA development workflow and an ISP tuning tool. To aid the hardware development process, we develop end-to-end reference designs for the KV260 vision AI starter kit (AMD Xilinx Kria SOM) and Efinix Ti180J484 kit, with support for 3 image sensors (Sony IMX219, Onsemi AR1335 and Omnivision OV5647) using the Infinite-ISP framework. These ISP reference designs support 10-bit 2592×1536 4 Megapixel (MP) Bayer image sensors with a maximum pixel throughput of 125 MP/s or 30 frames per second. We demonstrate that the image quality of Infinite-ISP is comparable to commercial ISPs found in Skype Certified Cameras and also performs competitively with digital still cameras in terms of the perceptual IQ metrics BRISQUE, PIQE, and NIQE. We envision the Infinite-ISP (available at https: // github. com/ 10x-Engineers/Infinite-ISP ) under the permissive open-source license to streamline ISP development from algorithmic design to hardware implementation and to foster community building and further research around it for both academia and industry.

We introduce an innovative 3D depth sensing scheme that seamlessly integrates various depth sensing modalities and technologies into a single compact device. Our approach dynamically switches between depth sensing modes, including iTOF and structured light, enabling real-time data fusion of depth images. We successfully demonstrated iToF depth imaging without multipath interference (MPI), simultaneously achieving high image resolution (VGA) and high depth accuracy at a frame rate of 30 fps.