Point Cloud Template Creation Elements, Principles, and Examples
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Instructions for Creating Target Object Point Cloud Templates
I. Prerequisites
| Prerequisite | Definition | Notes |
|---|---|---|
| Target Object pose variation | Limited poses (countable): only fixed faces of the Target Object can be seen (for example, the front side), and the valid Point Cloud area is the region of that face that remains stably visible. Arbitrary poses: the Target Object may appear at any angle, and the valid Point Cloud area is the region that can be imaged stably in more than 80% of poses (excluding deep holes, overexposed areas, grooves, and other parts where Point Cloud is easily missing). | Ordered scenes: The template should depend only on the Point Cloud of the fixed visible face to avoid matching failure caused by invisible faces. Random scenes: Stable visible regions must be determined through multi-view scanning or CAD simulation. Exclude regions where data is easily missing (such as reflective surfaces and deep concave structures) to avoid relying on unreliable Point Cloud. Confirmation method: Collect Point Cloud data under different poses, count the visibility frequency of each region, and screen out the valid Point Cloud area. |
| Camera Point Cloud imaging quality | The quality and completeness of the Point Cloud data obtained after the Target Object is recognized by a 3D Camera (such as structured light or binocular vision). | Missing Point Cloud Common causes: Target Object properties (high reflectivity or light-transmitting materials prevent the laser from returning). Ambient light interference (strong lighting causes sensor saturation). View occlusion (self-occlusion of the Target Object or occlusion by external objects). Optimization strategies: Adjust the light source: use diffuse light or polarized filtering to reduce reflections. Local template: use Point Cloud data from visible areas with better quality as the template. Discontinuous Point Cloud depth and severe noise Common causes: Target Object properties (multi-layer structures, transparent or translucent materials). The Camera is outside the disparity range (measuring too near or too far causes Point Cloud distortion). Stacked-object occlusion (for example, stacked workpieces cause Point Cloud mixing). Optimization strategies: Filtering and denoising: use filtering and outlier removal functions to optimize Point Cloud quality. Depth calibration: ensure the Camera collects data at the optimal working distance. Layered analysis: extract Point Cloud by region for stacked workpieces to avoid mismatching. |
II. Target Object Characteristics
| Attribute (higher priority means more important) | Definition | Notes |
|---|---|---|
| Size | The physical size of the Target Object (volume, area) affects Point Cloud collection density and template computation complexity. |
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| Self-occlusion | The structure of the Target Object itself causes some regions to fail to form Point Cloud under certain viewpoints (for example, multi-layer nesting, stacked workpieces, complex curved surfaces, and so on). |
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| Three-dimensionality | The degree of depth variation of the Target Object in 3D space (for example, height differences, stepped structures, curved undulations, and so on). |
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| Symmetry | The Target Object has a symmetric structure (for example, bilateral symmetry or rotational symmetry). |
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| Concave shape | The Target Object surface has recessed structures (such as inner holes or grooves), which may lead to missing Point Cloud or unstable imaging. |
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| Convex hull shape | The Target Object surface has recessed structures (such as inner holes or grooves), which may lead to missing Point Cloud or unstable imaging. |
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| Planar type | The Target Object surface is approximately planar and lacks obvious internal features. |
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III. Basic Principles
| When using model-based matching in industrial Robot 3D vision, feature selection should follow the principles below | |
|---|---|
| Characteristic | Description |
| Salience | Select features that are prominent and easy to identify on the Target Object surface, such as edges, corner points, textures, and concave or convex surfaces. These features should have strong discriminability to enable fast detection and matching in the scene. |
| Stability | Features should remain relatively stable under changes in lighting, viewpoint, and so on. Even if the Target Object rotates, scales, or deforms, these features should stay relatively unchanged. |
| Uniqueness | Features should be distinctive enough that they are not easily confused with features from other Target Objects or the environment. This helps improve matching accuracy and reliability. |
| Density | Distribute feature points as densely as possible on the Target Object surface to increase matching redundancy. Even if some feature points are occluded or lost, matching can still be completed using other feature points. |
| Repeatability | Select features that can be detected stably under different viewpoints and lighting conditions. This ensures that the features can be recognized accurately in various scenes. |
| Computational Efficiency | The extraction and description process for features should be as efficient as possible to meet the real-time response requirements of industrial Robots. Prefer features with relatively low computational cost when possible. |
IV. Notes for Specific Scenes
The general considerations for Point Cloud template creation under different industrial scenes and technical approaches are as follows.
| Scene | Scene Image | Point Cloud Image
| Template
| Notes for Point Cloud Templates: prerequisites × Target Object characteristics - basic principles - object selection - specific form | Recommended responses to the notes above |
|---|---|---|---|---|---|
| General Ordered | ![]() | ![]() ![]() | ![]() | Prerequisites: Target Object characteristics: large long-strip piece (possibly deformable), planar Target Object. Basic principles: | Prerequisites: Target Object characteristics: |
| General Random | ![]() ![]() | ![]() | Direct extraction image ![]() | Prerequisites: Target Object characteristics: self-occlusion, concave shape (with round holes). Basic principles: | Prerequisites: Target Object characteristics: |
| Surface-Type Ordered | ![]() ![]() | ![]() | ![]() | Prerequisites: Target Object characteristics: three-dimensionality (height differences and curved undulations). Basic principles: | Prerequisites: Target Object characteristics: |
| Surface-Type Ordered | ![]() Process error causes the spatial position of the small can to fluctuate up and down. | ![]() | ![]() | Prerequisites: Target Object characteristics: three-dimensionality (height differences and curved undulations). Basic principles: | Prerequisites: Target Object characteristics: |
| Surface-Type Random | ![]() ![]() | ![]() | ![]() | Prerequisites: Target Object characteristics: symmetry (rotational symmetry), self-occlusion. Basic principles: | Prerequisites: Target Object characteristics: |
| General Random | ![]() ![]() | ![]() | ![]() | Prerequisites: according to scene placement and Target Object characteristics, front and back templates should be used. Create separate front-side and back-side Point Cloud templates for the Target Object, and use overall keypoints for the Target Object. Target Object characteristics: convex-hull type; convex curved surfaces easily cause reflections and unstable Point Cloud imaging quality. Basic principles: | Prerequisites: use PickWiz to generate front-side and back-side Point Cloud templates separately (current viewpoint), and generate overall keypoints for the Target Object. Target Object characteristics: use dual scene Point Cloud templates, and the templates should fit the front and back sides of the CAD model. |
| Surface-Type Positioning Assembly (matching only) | ![]() | ![]() | ![]() | Prerequisites: Target Object characteristics: Basic principles: | Prerequisites: Target Object characteristics: |
| General Positioning Assembly | ![]() ![]() | ![]() | ![]() | Prerequisites: Target Object characteristics: Basic principles: | Prerequisites: Target Object characteristics: |













Process error causes the spatial position of the small can to fluctuate up and down.















