Many people, when watching cartoons as children, must have seen a scene where the protagonist draws an incredibly realistic tunnel on a wall, misleading opponents into crashing into it. Just last year, former NASA engineer Mark Rober used a similar tactic. He painted a three-dimensional road on a foam plastic wall and successfully tricked a Tesla in Auto-Pilot mode (with Autopilot enabled). During the experiment, the Tesla, traveling at 40 miles per hour, did not brake at all and crashed directly through the fake wall. In contrast, another vehicle equipped with LiDAR stopped steadily in front of the obstacle. This incident sparked strong public doubts about the safety of pure vision technology and prompted a reevaluation of its recognition capabilities in extreme optical illusions.

From a technological development perspective, early pure vision systems' inability to recognize such scenarios stemmed from neural network algorithms that processed three-dimensional space more like 'viewing photos' than 'perceiving the world.' Cameras capture photons and convert them into two-dimensional pixel matrices, losing depth information in the process. Traditional visual algorithms inferred distance by identifying feature textures, edge contours, and perspective relationships of objects. Realistic three-dimensional paintings exploit these visual cues to fake depth. However, as algorithm architectures evolved from rule-based modular designs to current end-to-end neural networks, coupled with hardware system improvements, visual perception systems' understanding of real three-dimensional space has undergone a qualitative change.

Reconstruction of Spatial Modeling Logic and Innovation in Occupancy Networks

For a visual perception system to understand three-dimensional paintings, it must first solve the problem of reconstructing three-dimensional geometric information from two-dimensional images. For a long time in the development of autonomous driving, most vehicle systems relied primarily on object detection technology. This means that the neural network attempts to find pixel blocks in the image that match the characteristics of 'lane lines,' 'vehicles,' or 'pedestrians' and assigns them a three-dimensional boundary. When a painting successfully simulates the texture of lane extensions and the horizon in the distance, the detector identifies these pixels as a drivable area because the system cannot find a matching 'obstacle' model in its library.

However, with the use of occupancy networks, the obstacle detection capability of pure vision autonomous driving has rapidly improved. This technology no longer focuses solely on specific object classifications but divides the space around the vehicle into tens of thousands of tiny cubic units, or voxels. The task of the occupancy network is to predict whether each voxel unit is occupied by an object or is in an idle state in three-dimensional space. In the latest technical patents, Tesla has further introduced high-fidelity occupancy determination technology and adopted a mathematical model known as the signed distance field. Unlike simple binary occupancy judgments, this model calculates the precise distance from any point in three-dimensional space to the nearest object surface. If the value is positive, the point is outside the object; if negative, it is inside; and if exactly zero, it represents the object's surface boundary.

This distance field-based modeling approach endows the visual system with stronger geometric sensitivity. By processing video streams from eight different-angle cameras, the system can calculate the subtle curvature and undulation of object surfaces. Even if a painting achieves perfection in color and texture, it remains physically a smooth plane. When the occupancy network incorporates signed distance field technology, it can identify the flatness of object surfaces with sub-voxel accuracy. When dealing with so-called 'three-dimensional fake roads,' the algorithm can detect logical conflicts between the 'perspective depth' portrayed in the image and the perceived 'planar geometry.'

Furthermore, hardware iterations have played a crucial role in enhancing recognition capabilities. With continuous hardware upgrades, the pixel density of cameras has significantly increased, allowing the system to capture printing dots, paper seams, or reflective characteristics on the canvas surface in 3D paintings. These tiny visual features, filtered out as noise by algorithms in the low-resolution era, have become key evidence for determining 'whether this is a painting' in the high-resolution era. Simultaneously, new computing chips provide stronger data processing capabilities, enabling the system to update the three-dimensional world model at higher frequencies and thus correct environmental cognitive biases in real-time.

Recognition Mechanism of Motion Difference and Spatiotemporal Fusion

If static occupancy networks expose deception from a spatial geometric perspective, then motion difference serves as the most powerful 'rangefinder' for pure vision solutions in dynamic environments. In human visual experience, when we move, objects close to us appear to move quickly in our field of view, while distant objects move slowly. This relative speed difference provides extremely reliable depth cues. Even if a person closes one eye, as long as they are moving, they will not be fooled by a wall painted with a road because, as they approach the wall, all pixel points in the painting will expand at the same speed, which is entirely inconsistent with the expansion speed of objects at different depths in a real three-dimensional scene.

In the latest visual software architecture, this biological principle has been transformed into a powerful spatiotemporal fusion algorithm. Previous systems processed each frame more like an independent photo, whereas current end-to-end networks process a continuous video stream. The system identifies a video queue containing dozens of image frames from the past few seconds. By comparing pixel displacements at different moments and angles, the neural network can accurately calculate the optical flow vector for each pixel point. When facing a wall painted with a three-dimensional road, the spatiotemporal fusion algorithm detects a logical flaw: the 'distant horizon' portrayed in the background of the painting exhibits optical flow characteristics identical to those of the nearby 'wall corner.' In the physical world, this is impossible.

This judgment of physical consistency is integrated into the system's world model. The world model, an internal simulator in the autonomous driving brain, continuously predicts the evolution of the surrounding environment over the next few seconds. When a vehicle accelerates toward a wall painted with a three-dimensional road, the world model expects to see rapid expansion of a plane. If the textures captured by the camera can express depth but their motion characteristics conform to the scaling law of a plane, the system's internal prediction error will surge. This triggers the system's defense mechanism, identifying it as a high-risk uncertain area.

Through these complex algorithmic collaborations, current pure vision systems are moving away from reliance on simple image classification. They learn to deconstruct surrounding scenes by observing changes in light and shadow, object displacement, and the coherence of geometric structures. This enhanced capability deepens the autonomous driving system's understanding of the rules governing the entire physical world.

Uncertainty and Safety Trade-offs in End-to-End Architecture

When discussing the recognition capabilities of visual systems, we must mention a significant shift in autonomous driving technology paths: the transition from rule-driven to data-driven end-to-end models. In rule-driven architectures, tens of thousands of lines of code are written to tell the car 'to stop if it sees a red circular sign.' This method has limitations, as the real world presents infinite combinations, making it impossible to predict every edge scenario. In contrast, current end-to-end systems integrate perception and decision-making into a massive neural network that learns to drive by studying real-life footage of experienced drivers.

This 'imitation learning' endows autonomous driving systems with stronger generalization capabilities. During training, the neural network has been exposed to countless real tunnels, overpasses, and highways, as well as various planar walls under changing light and shadow conditions. Through extensive learning, autonomous driving systems understand that a real physical opening exhibits specific statistical characteristics in light distribution, texture transitions, and detailed image changes as the vehicle approaches. When a three-dimensional painting appears, although it may mimic certain features well, it deviates from the statistical distribution of real driving scenarios in more dimensions.

Of course, when discussing end-to-end systems, the 'black box' issue must be addressed. When a vehicle in an end-to-end architecture identifies a fake wall and brakes, it is the result of the collaborative work of hundreds of millions of neurons, making it difficult to pinpoint which specific logic played a role. To increase system transparency and safety, researchers have added specialized 'visualization heads' to the neural network, rendering the AI's internal conception in real-time on the screen. This visualization is not just for passengers but also reflects the consensus-building process among internal system modules.

Final Words

The recognition capability of pure vision solutions for three-dimensional paintings is undergoing an evolution from 'completely passive' to 'active deconstruction.' With the refinement of occupancy networks, the application of spatiotemporal fusion technology, and the explosion of hardware computing capabilities, current visual systems have preliminary acquired (or 'have initially acquired') the ability to see through three-dimensional images. Although 100% recognition is not achievable, the technological evolution logic of pure vision autonomous driving is clear. Pure vision is no longer about interpreting images but about a holistic sensory reconstruction based on physical laws and dynamic observation. With further data accumulation and model expansion, future autonomous vehicles will possess keener vision than humans, capable of seeing through various edge scenarios.

-- END --