dToF vs iToF: Basic Principles To Real Uses

dToF vs iToF matters because it enables companies, by objectively weighing the pros and cons of each technology, to select the most suitable technical approach for real-world product development—taking into account specific ranging distances, environmental conditions, accuracy requirements, and cost constraints.

dToF vs iToF: Principles

–dToF Principle: A VCSEL emits pulse signals into the scene. The signals reflect off objects and are received by an SPAD. A TDC records the emission and reception times of the laser pulses. By calculating the time difference and multiplying it by the speed of light, the distance between the object and the sensor can be determined. Square-wave pulse modulation is typically used for the signal because it is relatively easy to implement with digital circuits.

–iToF Principle: A VCSEL emits periodic signals at a specific frequency. The signals reflect off objects and are received by an image sensor. The image sensor calculates the phase difference between the emitted and received signals, indirectly obtaining the time-of-flight of light, and then computes the distance between the object and the sensor.

	dToF	iToF
Principle	Directly measures the time-of-flight of light pulses	Measures the phase difference to indirectly calculate time
Distance Formula	d = c × t / 2	d = c × φ / (4πf)

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This article covers the essential differences between dToF and iToF. For a deeper dive into the technical principles, performance benchmarks, and real-world implementation strategies, please read the other two articles that provide a detailed introduction to dToF and iToF.

dToF vs iToF: Core Components

Comparison Dimension	dToF	iToF
Light Source	VCSEL (pulsed laser)	VCSEL or LED (modulated light)
Transmission Optics	Collimating lens / DOE	Diffuser plate
Receiver Sensor	SPAD (Single-Photon Avalanche Diode)	CAPD CIS (CMOS Image Sensor)
Time Measurement	TDC (Time-to-Digital Converter)	ADC + Phase Demodulation Circuit
Signal Processing	Histogram statistics + peak detection	Four-phase sampling + phase demodulation algorithm
Output Signal	Direct digital signal	Analog signal → ADC → digital processing
Pixel Process	SPAD (complex process, larger pixel size)	CMOS CAPD (mature process, smaller pixel size)

dToF vs iToF: Performance

Dimension	dToF (Direct)	Pulsed iToF	CW iToF (Phase)
Accuracy	★★★★★	★★★	★★★★
Long-range Performance	Excellent	Fair	Fair
Resolution	Medium	Medium	Relatively High (VGA-level)
Power Consumption	Relatively High	Medium	Relatively Low
Cost	Relatively High	Medium	Relatively Low
CMOS Compatibility	Challenging	Medium	Very Good
Multi-target Interference Rejection	Strong	Fair	Fair
Maturity	Rapidly Developing	Less Commonly Used	Most Mature
Representative Products	iPhone LiDAR	Industrial Sensors	Kinect, Android Phone TOF

dToF vs iToF: Advantages and Disadvantages

Type	Advantages	Disadvantages
dToF	High accuracy: Since it directly measures the time-of-flight, dToF offers high ranging accuracy, making it suitable for scenarios requiring high-precision measurements.	Higher hardware requirements: requires high-precision clocks for measurement; needs to generate high-frequency, short-duration, high-intensity pulsed optical signals.
	Long-range measurement: dToF can measure at relatively long distances, making it suitable for applications requiring long-range distance sensing.	The optical pulse signals detected by dToF are typically at the nanosecond or picosecond level, so SPADs need to achieve high precision.
	Strong anti-interference capability: dToF is relatively insensitive to ambient light interference and can maintain stable measurement performance in complex environments.	Compared to CMOS image sensors, SPADs generally have lower integration levels, resulting in relatively poor resolution and higher cost.
	dToF depth algorithms are relatively simple.
iToF	Suitable for short-range distance measurement.	Lower accuracy: detection range is relatively short, and accuracy degrades as distance increases.
	Since iToF sensor pixels are relatively small, they can achieve relatively high image resolution.	High power consumption: power consumption increases with distance.
	Low cost.	Susceptible to ambient light interference.
		Depth algorithms are relatively complex.

dToF vs iToF: Applications

–dToF：
dToF features low power consumption and a compact size, making it more suitable for applications such as industrial robots that require fast ranging and obstacle detection, as well as other space-constrained compact designs.

dToF demonstrates good resistance to environmental interference. Currently, its ranging accuracy in outdoor scenarios is higher than that of iToF, giving it an advantage in outdoor application scenarios.

–iToF
iToF offers higher image resolution, enabling it to reproduce more detailed scene information in application scenarios such as object recognition, 3-Dimensional reconstruction, and behavior analysis. It holds advantages in fields like robotics and new retail.

Application	Distance	Recommended Solution	Reason
Robot Vacuum Obstacle Avoidance	<5m	iToF	Low cost, high resolution, good frame rate
Smartphone	<1m	dToF	Strong ambient light resistance, low power consumption
Automotive LiDAR	>100m	dToF	Long range, strong ambient light resistance
Drone Obstacle Avoidance	<20m	dToF	Strong ambient light resistance, long range
Proximity Sensing	<0.5m	iToF	Low cost

dToF vs iToF: Technological Breakthroughs

iToF

–Pixel Process and Resolution Enhancement: Since 2018, iToF pixel sizes have continued to shrink, from the early 10μm level down to the current 3.5μm level; resolution has progressively advanced from QVGA towards VGA and megapixel levels. In 2020, Sony launched a 3-dimensional stacked back-illuminated structure iToF sensor with a resolution of 1290×960. In 2024, MIT research achieved an iToF pixel structure with a resolution of 1290×1080.

–Accuracy and Modulation Frequency Upgrades: By increasing modulation frequencies and employing multi-frequency fusion algorithms, short-range ranging accuracy has improved from centimeter-level to millimeter-level.

–Power Consumption and Integration Optimization: Pixel-level integration processes continue to mature, chip area continues to shrink, and combined with dynamic power regulation technology, they adapt to the low-power requirements of mobile devices.

dToF

–Significant Resolution Leap: Early dToF focused on single-point or low-resolution arrays. For example, ST’s first-generation product only offered single-point ranging, and ams OSRAM’s previous-generation product had an 8×8 array. In 2025, ams OSRAM released the TMF8829, increasing resolution from 64 points to 1536 points (48×32)—a nearly 24-fold increase in sampling points—significantly improving spatial modeling and object recognition accuracy. ADI’s ADTF3175 achieves 1024×1024 (1 megapixel) area-array dToF imaging with depth accuracy of ±5mm, capable of capturing subtle object shapes and motion trajectories.

–SPAD Process and TDC Accuracy Upgrades: Single-photon detection efficiency continues to improve, and the time resolution of Time-to-Digital Converters (TDCs) has entered the picosecond level, supporting longer-range ranging and higher accuracy.

–Stratified Product Portfolio Enhancement: Two main product series have emerged—single-zone high-precision ranging and multi-zone spatial perception—covering ranging needs from 1cm to 5 meters, suitable for differentiated scenarios such as smartphone autofocus, gesture recognition, and spatial modeling.

dToF vs iToF: Respective Technology Development Directions

–dToF：

Direction	Description
SPAD Pixel Miniaturization	Improve spatial resolution
3-Dimensional Stacking Process	Vertical stacking of SPAD + TDC + logic circuits
All-Solid-State LiDAR	Flash / OPA scanning
1550nm Wavelength	Longer detection range

–iToF

Direction	Description
Higher Modulation Frequency	Improve accuracy
Multi-Frequency Fusion Algorithm	Mitigate multipath interference
Pixel-Level Integration	Higher resolution (1MP+)
Low-Power Design	Optimize battery life for mobile devices

dToF vs iToF: Technology Fusion

dToF and iToF technologies each have their own strengths and weaknesses. dToF offers stable long-range measurement and strong anti-interference capability, but has lower resolution and complex circuitry that is difficult to integrate. iToF offers high resolution and ease of integration, but its measurement range is limited and its anti-interference capability is weak. Hybrid ToF systems can combine the advantages of both and compensate for their respective shortcomings.

–Hardware Fusion: dToF pixels (SPADs) and iToF pixels (CMOS CAPDs) are interleaved or mixed on the same sensor chip, enabling simultaneous acquisition of both types of data on a single chip. For example, dToF pixels provide long-range absolute distance and anti-interference capability, while iToF pixels provide dense depth and texture information at close range—the two complement each other to generate a more complete depth map.

–Algorithm Fusion: In the same optical system, time-division or zone-division methods are used to perform ranging with iToF and dToF separately, and then the data is fused through algorithms. For instance, dToF is used to provide long-range “depth anchors” to eliminate iToF’s “distance ambiguity,” while iToF’s high resolution is used to densify and complete the sparse point cloud from dToF.

–Multi-Frequency Fusion: Combining both technologies can further enhance ranging robustness in complex scenarios (such as specular reflections and concave corners).

In recent years, some hybrid ToF products have also emerged.

Infineon’s REAL3 flexible ToF imager technology integrates existing high-resolution iToF flood illumination and dToF long-range point-source illumination into a single hybrid ToF camera. By adding precise long-range spot data, it creates an accurate 3D map of the surrounding area.

Toppan’s hybrid ToF achieves distance measurement of up to 30 meters. Toppan’s hybrid ToF focuses on enhancing system robustness. Compared to traditional iToF systems, its hybrid system increases sensing distance, especially in outdoor applications.

Image Credits: Researchgate & Erdicn & Wiseomeinc & AI & Maxictech & Piecarte & Electronics-lab & Automate