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NeRF Ray Sampling

HardCV

Implement ray sampling for Neural Radiance Fields (NeRF), a technique used to represent 3D scenes as continuous functions. This involves generating rays for each pixel in an image, given camera parameters such as position and orientation.

The process relies on projective geometry, where each pixel's direction is calculated using the camera's intrinsic matrix $K$ and the pixel's coordinates. The ray origin is typically the camera position, while the ray direction is from the camera through the pixel.

Here are the key steps:

Calculate the pixel directions using the camera's intrinsic matrix.
Transform the directions by the camera's rotation.
Sample points along each ray for volume rendering.

\begin{pmatrix} x \\ y \\ z \end{pmatrix} = \begin{pmatrix} x_d \\ y_d \\ z_d \end{pmatrix} t + \begin{pmatrix} x_o \\ y_o \\ z_o \end{pmatrix}

This technique is widely used in computer vision and 3D reconstruction applications.

Example:

Input:

Camera pose, K, image size

Output:

Ray origins and directions

Reasoning:

Compute ray direction for each pixel using inverse K

Constraints:

Input parameters: camera pose (3D numpy array), intrinsic matrix K (3x3 numpy array), image size (2-element tuple of integers)
Valid ranges: camera pose and intrinsic matrix values are floating-point numbers, image size values are positive integers, pixel coordinates are in the range [0, image_size)
Output format: return ray origins as a 2D numpy array of shape (image_size, image_size, 3) and ray directions as a 2D numpy array of shape (image_size, image_size, 3), both with floating-point precision
Special conditions: assume the input camera pose is in the format [x, y, z] and the intrinsic matrix K is in the standard pinhole camera format, with no lens distortion or other aberrations

Editor

Python 3.13.1

Test Results

0/0

Run code to see test results.