https://github.com/smrfeld/chamfer-viz
Visualization of Chamfer distance between point clouds using Dash app
https://github.com/smrfeld/chamfer-viz
chamfer chamfer-distance dash distance-measures machine-learning plotly point-cloud
Last synced: 6 months ago
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Visualization of Chamfer distance between point clouds using Dash app
- Host: GitHub
- URL: https://github.com/smrfeld/chamfer-viz
- Owner: smrfeld
- License: mit
- Created: 2023-12-15T06:14:34.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-15T21:02:50.000Z (almost 2 years ago)
- Last Synced: 2025-02-16T06:44:58.012Z (8 months ago)
- Topics: chamfer, chamfer-distance, dash, distance-measures, machine-learning, plotly, point-cloud
- Language: JavaScript
- Homepage: https://www.oliver-ernst.com/chamfer-viz/
- Size: 1.51 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Chamfer distance visualization
Visualization of the Chamfer distance between two point clouds using `dash` app in **both** Python and Javascript.
[https://www.oliver-ernst.com/chamfer-viz/](https://www.oliver-ernst.com/chamfer-viz/)
## Running
### Javascript
Navigate to the `js` directory and run using VSCode's Live Server extension or any other web server of your choice. For example:
```bash
cd js
python -m http.server
```
### Python
Navigate to the `python` directory and install the requirements:
```bash
pip install -r requirements.txt
```
Then run the app:
```bash
python app.py
```
Optionally: specify the port with `--port` flag or debug mode with `--debug` flag.
## Explanation
1. Given two sets of points or shapes, usually referred to as Set A and Set B.
2. For each point in Set A, find the closest point in Set B and calculate the distance between them.
3. For each point in Set B, find the closest point in Set A and calculate the distance between them.
4. Sum up all these distances from steps 2 and 3 to obtain the Chamfer distance.
Mathematically, the Chamfer distance can be expressed as:
```math
Chamfer distance = Σ(min(dist(A_i, B_j)) + Σ(min(dist(B_i, A_j)))
```
Where:
`A_i` represents a point in Set A.
`B_j` represents a point in Set B.
`dist(A_i, B_j)` is the distance between point `A_i` and its closest point in Set B.
`dist(B_i, A_j)` is the distance between point `B_i` and its closest point in Set A.
## Implementation
Chamfer distance uses a KDTree. A KDTree is a data structure that organizes points in a way that enables fast nearest-neighbor queries. Computing the structure for Set B makes it faster to find the nearest neighbors in Set B for each point in Set A (and vice versa).
The use of the KDTree data structure significantly speeds up the computation of nearest neighbors for each point in Sets A and B. Without KDTree, you would need to perform a brute-force search, which can be computationally expensive, especially for large point sets. KDTree organizes the points in a way that allows for efficient nearest-neighbor queries and is commonly used in computational geometry and various data science tasks.
It's important to note that while KD-Trees offer efficient nearest neighbor and range search capabilities, their performance can degrade for very high-dimensional data (often referred to as the "curse of dimensionality"). In such cases, other data structures like ball trees or approximate nearest neighbor algorithms may be more suitable. Nevertheless, KD-Trees remain a valuable tool for solving a wide range of problems involving multi-dimensional data.
## References
* [KDtree in JS](https://github.com/ubilabs/kd-tree-javascript)
* [Basic Chamfer distance implementation](https://medium.com/@sim30217/chamfer-distance-4207955e8612)
```python
import numpy as np
from scipy.spatial import KDTree
def chamfer_distance(A, B):
"""
Computes the chamfer distance between two sets of points A and B.
"""
tree = KDTree(B)
dist_A = tree.query(A)[0]
tree = KDTree(A)
dist_B = tree.query(B)[0]
return np.mean(dist_A) + np.mean(dist_B)
```