ToF Cameras Compared to Other 3D Depth camera:How to choose?

Nowadays, whether it is face recognition, intelligent robots or autonomous driving, they all rely on the depth perception of the surrounding environment. To this end, a series of advanced 3D depth perception technologies have emerged, including mainstream ones such as ToF cameras, structured light cameras, stereo vision cameras and LiDAR. So, I believe everyone has had such a question when choosing a 3D depth camera: Which 3D camera meets my needs? ToF or other 3D depth cameras?

So, in this article, we will deeply understand the characteristics and differences between ToF and other 3D depth cameras, and why ToF is more popular than other 3D depth cameras.

What is 3D depth perception? 3D depth mapping, also called depth sensing or 3D mapping, creates an image of a three-dimensional space or target object by measuring the distance between the sensor and every point in the surrounding environment. The technology involves projecting light onto the shutter object and capturing the reflected light with a camera or sensor.

Then, by analyzing the timing or pattern of the reflections, the distance between the camera and different parts of the scene is calculated to create a depth map. The depth map is essentially a digital representation of how far away each part of the scene is from the perspective of the sensor.

3D depth camera module

It is precisely because of this feature of 3D depth cameras that it has played an immeasurable role in multiple industries. So, before comparing ToF and other 3D map technologies, let's take a look at their characteristics one by one.

In previous articles, we have learned that Time of Flight is simply the time it takes for light to reach an object, reflect, and return to the sensor. Through this measurement, a ToF camera can determine the distance of various objects in the scene from itself. The main components of a ToF camera include the ToF sensor and the sensor module, which captures the reflected light and converts it into data that the camera can process.

They usually utilize light sources such as VCSELs or LEDs that emit light in the near-infrared (NIR) spectrum. In addition, the depth sensor processes this raw data, filtering out noise and other inaccuracies to provide clear depth information.

The working principle of a ToF camera is like a "light echo rangefinder". It emits a known light pulse (usually infrared light), and then accurately measures the time it takes for the light pulse to be reflected back by the object. Based on the principle that the speed of light is constant, the distance the light travels can be calculated, thereby generating depth information for each point in the scene.

Its advantages are strong real-time performance and the ability to quickly obtain depth maps; compact size and relatively low power consumption; low dependence on the surface texture of the target object, and it can work even on weakly textured surfaces.

For more details about ToF cameras, please read: ToF sensor: working principle and core component analysis.

Stereo vision mimics the way the human eye works, and stereo disparity refers to the difference in the position of the image of an object seen by the left and right eyes. It uses two (or more) cameras to simultaneously capture images of the same scene from slightly different perspectives. The system uses the principle of triangulation to calculate the depth of an object by identifying the disparity between corresponding points in the two images - that is, the difference in the relative position of the object in the two images, combined with the geometric parameters of the camera (such as the baseline distance). Two concepts are needed for this:

Baseline: This is the distance between the two cameras (about 50–75 mm - pupil distance).
Resolution: Proportional to depth. The more pixels searched, the higher the number of disparity levels (but the computational load is also higher).

Focal length is proportional to depth of field. The shorter the focal length, the farther you can see, but the field of view will also be reduced. The higher the focal length, the closer the depth of field and the larger the field of view. Stereo vision cameras are based on this technology.

The advantages of this technology are that it is relatively low cost, especially when no active light source is required; it can capture color images and depth information.

Structured light technology projects known light patterns, such as dots, stripes, or coded patterns, into a scene, and then uses a camera to capture the distortion of these patterns on the surface of an object due to depth changes. By analyzing these distortions and using the principle of triangulation, the system can accurately calculate the three-dimensional shape and depth information of the object.

Structured light cameras are also based on this technology, using specially designed projected patterns to enhance the camera's ability to identify and measure changes in the surface it illuminates. By processing changes in pattern distortion to calculate the distance from the camera to each point on the surface of the object, a 3D map of the object is created.

how Structured Light Imaging

This can provide very high depth accuracy at close range (usually within 1 meter), especially suitable for applications with rich details, such as 3D scanning, gesture recognition, etc.

LiDAR works in a similar way to ToF, and also determines distance by emitting laser beams and measuring their flight time. But unlike ToF cameras, which usually capture the depth of an area, LiDAR systems usually emit discrete laser beams and scan them to build an extremely detailed, high-resolution point cloud map. Depending on the type, LiDAR can be divided into mechanical (with rotating parts) and solid-state.

The advantages of this are long detection distances, up to hundreds of meters; extremely high accuracy, especially suitable for large-scale environmental perception and high-precision mapping; and excellent performance in strong outdoor light.

Each 3D imaging technology camera has its own advantages and disadvantages. Below we use a chart to compare the difference between ToF cameras and other 3D stereo vision cameras.

Feature Dimension	ToF Camera (Time-of-Flight)	Structured Light	Stereo Vision	LiDAR
Working Principle	Measures the round-trip time of light pulses	Projects known pattern, analyzes distortion to calculate depth	Dual or multi-camera setup, calculates depth via disparity	Scans and emits laser beams, measures time of flight
Accuracy	Millimeter to centimeter level	Micrometer to millimeter level (excellent at close range)	Centimeter level (highly affected by texture, distance)	Millimeter to centimeter level (high accuracy over long distances)
Detection Distance/Range	Medium to short range (several meters to tens of meters)	Short range (typically within 1 meter)	Medium to short range (affected by baseline)	Long range (tens to hundreds of meters)
Ambient Light Adaptability	Active illumination, some anti-interference; performance degrades in strong direct light	Active illumination, patterns easily "washed out" by strong sunlight	Passive, heavily relies on ambient light and texture; poor performance in low light	Active illumination, strong resistance to ambient light
Outdoor Performance	Challenge is infrared interference from sunlight; requires additional processing	Prone to sunlight interference	Relies on natural light; lack of texture is a problem	Typically performs best outdoors
Computational Complexity	Directly outputs depth, relatively low	Requires pattern deformation analysis, medium complexity	Requires complex feature matching, high complexity	Large data volume, but point cloud processing is relatively direct
Size & Complexity	Usually most compact, no mechanical parts	Includes projector and camera, moderate size	Two cameras, moderate size	Usually larger, some with mechanical rotating parts
Cost	Relatively cost-effective	Medium	Lowest (if using existing cameras)	Usually highest
Typical Applications	Mobile AR/depth sensing, robot obstacle avoidance, gesture recognition, in-car monitoring	Face recognition unlock, precision measurement, industrial inspection	Robot navigation, drone obstacle avoidance, AR/VR (in textured scenes)	Autonomous driving, high-precision mapping, smart cities

The table above gives us a preliminary understanding of the differences between ToF cameras and other 3D depth cameras. So, why are ToF cameras a better choice for 3D measurement? We have summarized the following factors:

Higher imaging accuracy: Due to the reliance on precise light source illumination, ToF cameras provide better image quality output.
Reduced software complexity: ToF cameras provide depth data directly from the module, avoiding complex situations such as running depth matching algorithms in the host platform.
Higher depth scalability: ToF cameras have a scalable depth range with the number of VCSELs used for illumination.
Better low-light performance: ToF cameras perform better in low-light conditions because of active and reliable light sources.
Compact size: Because the sensor and illumination can be placed together, ToF cameras have a more compact form factor.

Mobile AR (augmented reality)/photo depth effect: Mobile phones need to be thin, light, low power, and able to capture depth information in real time. ToF cameras are ideal because of their small size, good real-time performance, and low texture dependence.
Gesture recognition/spatial positioning in VR/AR headsets: Real-time, accurate gesture tracking and indoor environment perception are also required. ToF cameras can provide low-latency depth data, which is very suitable for such interactive applications.
Driver monitoring/gesture recognition: Usually used in the car for short distances, with requirements for real-time performance, size, and cost. ToF cameras are a very suitable solution.
Object recognition/sorting: Identify the shape, size, and position of objects so that robots can grasp and classify them. ToF cameras can quickly provide three-dimensional contour information of objects.

The use of Lidar in intelligent driving

To learn more about the role of camera modules in specific applications, read:

What is an Autofocus Camera Module?How does it work?

What is a Low Light Camera Module? understand in depth

To learn more about machine vision systems, read:

Embedded Vision vs. Machine Vision: Understanding the Key Differences

Faced with a variety of 3D depth sensing technologies, we should understand how to choose the one that suits our project needs. Here are some key considerations:

Clear application requirements: This is the first and most critical step.
Accuracy requirements: Does your application require millimeter-level, centimeter-level, or lower accuracy?
Detection distance: Do you need to sense close distance (tens of centimeters), medium distance (several meters), or long distance (tens of meters or more)?
Environmental conditions: Will your system work indoors, outdoors, in strong light, low light, or in complete darkness?
Real-time requirements: How fast does the data need to be updated?
Target object characteristics: Is the target transparent, reflective, richly textured, or smooth and textureless?
Data output: Do you need a depth map, point cloud, or other format?
Consider budget and size constraints: Cost and physical size are often factors that cannot be ignored in actual projects. Some high-precision, long-distance solutions can be expensive and bulky.
Assess data processing complexity and development difficulty: Some technologies may generate massive amounts of data, requiring more powerful computing resources and more complex algorithms to process.
Testing and verification: Prototype testing and verification in your actual application scenarios as much as possible is the best way to evaluate technology performance and feasibility.

When choosing a camera module, the interface type, supplier, customization requirements, etc. are also important. Please read:

The factor to choosing the correct interface for an embedded vision system?

How to Choose the Ideal Camera Module for Your Vision System: A Step-by-Step Guide

Standard Camera Modules vs. Custom Camera Modules: How to Choose the Right Fit

What is OEM Camera Modules? Understanding Custom Solutions for Product Manufacturers

The Advantages of Choosing Custom Camera Modules for Product Development

In the field of 3D depth perception, no single technology is a panacea. ToF cameras, structured light, stereo vision, and LiDAR each have unique technical advantages and applicable scenarios. ToF shines in short- and medium-range applications with its real-time and compactness; structured light is unmatched in close-range accuracy; stereo vision provides flexible solutions with its cost-effectiveness and passive characteristics; and LiDAR dominates in complex outdoor environments with its long distance and high accuracy. We ultimately have to make choices based on actual project needs.

Of course, if you have trouble finding a suitable 3D imaging camera, please contact us. Muchvision has more than ten years of experience in the field of embedded vision and has manufactured many high-performance camera modules, including modules for 3D measurement cameras. I believe that with our professional team of engineers, we can find the right solution for you.

Q1: Are ToF and LiDAR the same thing?
A1: Not exactly. ToF (time of flight) is a distance measurement principle, and LiDAR (laser radar) is a system that applies the ToF principle. All LiDARs use the ToF principle to measure distance, but ToF sensors and ToF cameras usually refer to more compact and lower-cost solutions, often used in shorter distance scenarios. LiDAR usually refers to systems that emit and scan laser beams and build high-density point clouds to achieve high-precision measurements at long distances (such as for autonomous driving). You can understand that LiDAR is a high-end application of the ToF principle in more complex and larger systems.

Q2: Which 3D depth sensor is the most accurate?
A2: It depends on the distance. At very close distances (tens of centimeters to 1 meter), structured light usually provides the highest depth accuracy, up to micron or even sub-millimeter levels. At medium and short distances (several meters to tens of meters), ToF cameras can provide real-time depth data at the millimeter to centimeter level. At long distances (tens to hundreds of meters), LiDAR can maintain excellent millimeter to centimeter accuracy and obtain high-density point clouds. The accuracy of stereo vision is limited by the baseline distance and image texture, usually at the centimeter level.

Q3: Which one is better for outdoor use, structured light or ToF?
A3: In general, ToF cameras perform better than structured light in strong outdoor light. Structured light technology relies on projected patterns, which can easily be "washed out" or submerged by ambient light in strong sunlight, resulting in inaccurate recognition. Although ToF cameras are also affected by the infrared component of sunlight, they are relatively more robust through narrow-band filters and algorithm optimization. LiDAR performs best outdoors because it usually uses higher-power lasers and more advanced filtering technology.