NeRF Rendering With Mobile Sensor Array And Depth Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating three-dimensional renderings of surgical scenes using neural radiance fields (NeRF) are computationally intensive and do not support real-time rendering, requiring extensive training times and large arrays of cameras, which limits their application in immersive and interactive surgical environments.
Innovation Solution
A mobile sensor array system that moves via a robotically-controlled mover, capturing RGB and depth data at multiple positions, uses registration transformations to quickly determine camera poses, and employs a NeRF algorithm constrained by a unified depth map to render photorealistic 3D images from novel perspectives, enabling real-time or near real-time rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional NeRF methods are used to generate photorealistic 3D renderings, then rendering quality is improved, but computational time and processing speed worsen (training takes many hours to days)
Solution Approach 1:
The system performs preliminary actions by capturing images from multiple camera positions before the surgical procedure begins. These pre-captured images are used to train the NeRF model offline, so that during the actual surgery, the system can quickly generate real-time 3D renderings without requiring extensive training time. This separates the computationally intensive training phase from the time-sensitive rendering phase.
Solution Approach 2:
The system dynamically adapts the NeRF rendering process by using a mobile sensor array that can be repositioned during surgery to capture images from different angles. The system updates the 3D model in real-time based on new image data, allowing the rendering quality to be maintained or improved while reducing the need for lengthy re-training sessions.
2Manufacturing precision
If large arrays of cameras are used to capture sufficient input views for NeRF, then rendering accuracy is improved, but device complexity and cost worsen (requiring tens to hundreds of cameras)
Solution Approach 1:
The system replaces a static large-array camera system with a mobile sensor array that can be dynamically repositioned. By moving a smaller number of cameras to multiple positions around the surgical site, the system captures diverse viewing angles necessary for accurate NeRF rendering without requiring tens or hundreds of fixed cameras simultaneously.
Solution Approach 2:
The system adds the temporal dimension to the camera array configuration by capturing images at multiple time points as the sensor array moves to different positions. This transforms the problem from requiring many cameras at one position to using fewer cameras at multiple positions and times, thereby reducing device complexity while maintaining rendering accuracy.
3Productivity
If real-time rendering is implemented, then surgical efficiency is improved, but computational resource requirements worsen (needing faster processing hardware)
Solution Approach 1:
The system performs computationally intensive NeRF model training in advance, before the surgical procedure begins. This preliminary training phase can utilize high computational power without impacting surgical efficiency. Once trained, the model can generate real-time renderings during surgery with significantly reduced computational requirements, thus improving surgical efficiency without needing sustained high computational power during the procedure.
Data Source
AI summary
Methods of generating three-dimensional (3D) views of a scene, such as a surgical scene, and associated systems and devices are disclosed herein. In some embodiments, a representative method includes moving a sensor array about a target volume and capturing RGB image data and depth data of the target volume with multiple cameras and a depth sensor of the sensor array, respectively. Poses of the RBG cameras and the depth sensor can be determined at each position. The captured RGB image data and the RGB camera poses can be used to train a radiance volume of a neural radiance field (NeRF) algorithm, and the depth data can be used to constrain the training of the NeRF algorithm. The NeRF algorithm can render a 3D image of the target volume based on a specified observer pose.