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A vision chip with complementary pathways for open-world sensing


Image sensors face substantial challenges when dealing with dynamic, diverse and unpredictable scenes in open-world applications. However, the development of image sensors towards high speed, high resolution, large dynamic range and high precision is limited by power and bandwidth. Here we present a complementary sensing paradigm inspired by the human visual system that involves parsing visual information into primitive-based representations and assembling these primitives to form two complementary vision pathways: a cognition-oriented pathway for accurate cognition and an action-oriented pathway for rapid response. To realize this paradigm, a vision chip called Tianmouc is developed, incorporating a hybrid pixel array and a parallel-and-heterogeneous readout architecture. Leveraging the characteristics of the complementary vision pathway, Tianmouc achieves high-speed sensing of up to 10,000 fps, a dynamic range of 130 dB and an advanced figure of merit in terms of spatial resolution, speed and dynamic range. Furthermore, it adaptively reduces bandwidth by 90%. We demonstrate the integration of a Tianmouc chip into an autonomous driving system, showcasing its abilities to enable accurate, fast and robust perception, even in challenging corner cases on open roads. The primitive-based complementary sensing paradigm helps in overcoming fundamental limitations in developing vision systems for diverse open-world applications.

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Fig. 1: The challenges of open-world visual sensing and the solution with the complementary vision paradigm.
Fig. 2: The architecture of the Tianmouc chip.
Fig. 3: Summary of chip evaluation.
Fig. 4: Open-world perception experiments.

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Data availability

The data supporting the findings of this study are available in the main text, Extended Data, Supplementary Information, source data and Zenodo ( data are provided with this paper.

Code availability

The algorithms and codes supporting the findings of this study are available at Zenodo (


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This work was supported by the STI 2030—Major Projects 2021ZD0200300 and National Natural Science Foundation of China (no. 62088102).

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Authors and Affiliations



Z.Y., T.W. and Y.L. were in charge of the Tianmouc chip architecture and chip design, the Tianmouc chip test and system design, and algorithm and software design, respectively. L.S. and R.Z. proposed the concept of a complementary vision paradigm, and Z.Y., Y.L., T.W. and Y.C. conducted the related theoretical analysis. T.W., J.P., Y.Z., J.Z., X.W. and X.L. contributed to the chip design. Y.C., H.Z., J.W. and X.L. contributed to the chip test. Z.Y., Y.L., T.W. and Y.C. contributed to the autonomous driving system design. All authors contributed to the experimental analysis and interpretation of results. R.Z., L.S., Z.Y., T.W., Y.L. and Y.C. wrote the paper with input from all authors. L.S. and R.Z. designed the entire experiment and supervised the whole project.

Corresponding authors

Correspondence to Rong Zhao or Luping Shi.

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The authors declare no competing interests.

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Nature thanks Craig Vineyard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 The complementarity of the Human Vision System (HVS).

The retina is composed of rod and cone cells that operate in an oppositional manner to expand the sensitivity range. At the next level, in the lateral geniculate nucleus (LGN), the M-pathway and P-pathway encode information in a complementary manner. The output information from the LGN is then reorganized into a series of primitives, including colour, orientation, depth, and direction at the V1 region. Finally, these primitives are transmitted separately to the ventral and dorsal pathways to facilitate the recognition of objects and visual-guided behavior.

Extended Data Fig. 2 Tianmouc architecture.

a, Schematic of the pixel structure in the back-side illuminated hybrid pixel array. b, Schematic of the cone-inspired and rod-inspired pixels. c, Schematic of readout circuits of the COP and AOP. d, Schematic of compressed packets generation process through the sparse spatiotemporal difference packetizer.

Extended Data Fig. 3 Tianmouc chip testing systems.

a, Testing boards equipped with a Tianmouc chip. b, The full system to process the output data of Tianmouc chip. The data is first transmitted to the FPGA board, where it collects raw data before transferring it to the host computer through PCIe. Subsequently, the host takes the charge of data processing for test and other tasks.

Extended Data Fig. 4 Experimental setup for chip characterization.

a, Schematic illustration of the experimental set-up for the chip evaluation based on EMVA1288. b, A photograph of the optical setup. c, Photograph of the chip evaluation system including chip test board, FPGA board, host computer and the high-speed ADC acquisition card. d, Schematic illustration of the optical set-up for dynamic range measurement. e, A photograph of the optical setup for dynamic range measurement.

Extended Data Fig. 5 Chip characterization.

a, High-speed recording of an unpredictable and fast-moving ping-pong ball shot by a machine. b, Power consumption of Tianmouc. The left half depicts the distribution of different modules including pixel, analog, digital and interface circuits. The right illustrates the total power consumption under different modes. c, Anti-aliasing reconstruction of the rotation of a wheel. The alias in the wheel recorded by COP can be eliminated by the high-speed AOP. d, the AOP of Tianmouc is able to capture lightning that is missed by COP and record details of textures.

Source Data

Extended Data Fig. 6 The reconstruction pipeline.

a, The structure of the whole reconstruction network. b, The light-weight optical flow estimator modified from SpyNet, using multi-scale residual flow calculation. In this figure, d means down-sampling operation. c, A self-supervised training pipeline, where we use the two colour images and the difference data between these two images to provide two training samples. d, At the inference stage, we adjust the amount of input data to obtain high-speed colour images at any time point.

Extended Data Fig. 7 The streaming perception pipelines for the open-world automotive driving tasks.

In Tianmouc, different primitive combinations are encoded to form the AOP and COP. These two pathways maintain separate buffers and support independent feedback control. The processed data of the AOP and the COP are then sent to different NN or an optical flow solver. Subsequently, the inference results are integrated in a multi-object tracker. This approach optimally leverages the CVP at a semantic level, preserving both low-latency response ability and high performance simultaneously.

Extended Data Fig. 8 More cases demonstrate the efficiency of Tianmouc in adapting to the open world.

The sparse data in the AOP, coupled with the encoding method, enables Tianmouc to adaptively adjust its transmission bandwidth, typically maintaining it at a bandwidth below 80 MB/s in most scenarios. With the complementary perception paradigm, this bandwidth proves adequate for efficiently addressing diverse corner cases.

Source Data

Extended Data Table 1 The primitive-based representation and complementary sensing paradigm in Tianmouc
Extended Data Table 2 Comparison of Tianmouc with existing vision sensors

Supplementary information

Supplementary Information

The Supplementary Information file contains Supplementary Notes 1–9 and Supplementary Tables 1–2.

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Yang, Z., Wang, T., Lin, Y. et al. A vision chip with complementary pathways for open-world sensing. Nature 629, 1027–1033 (2024).

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