Currently, there are no specific software tools exclusively designed for the MacBook Pro M4 that support neural surface reconstruction. However, several technologies and frameworks can be adapted or utilized on the M4 due to its powerful M4 chip, which includes a Neural Engine that enhances AI performance.
Available Technologies and Frameworks
1. Apple's Object Capture API: While not specifically designed for neural surface reconstruction, Apple's Object Capture API is a photogrammetry tool that can be used on Apple Silicon devices, including the MacBook Pro M4. It allows users to create 3D models from a set of 2D images, but it requires complete coverage of the scene or object[7].
2. Neural Surface Reconstruction Frameworks: Although not native to the MacBook Pro M4, frameworks like SparseNeuS and Neural RGB-D Surface Reconstruction can potentially be run on the M4 due to its computational capabilities. These frameworks use neural rendering techniques to reconstruct surfaces from sparse views or RGB-D data[5][9].
3. NVIDIA's Neural Surface Reconstruction Tools: While primarily designed for NVIDIA hardware, tools like Neural Kernel Surface Reconstruction (NKSR) and Neuralangelo are cutting-edge in the field of neural surface reconstruction. However, they are optimized for NVIDIA GPUs and might not run natively on the MacBook Pro M4 without significant modifications or emulation[1][2].
Utilizing the MacBook Pro M4 for Neural Surface Reconstruction
The MacBook Pro M4's powerful Neural Engine and GPU can support various AI workloads, making it a viable platform for running neural surface reconstruction tasks if the software is adapted or optimized for Apple Silicon. Developers could leverage the M4's capabilities by porting or developing compatible software using frameworks like PyTorch or TensorFlow, which support Apple's Metal API for GPU acceleration.
In summary, while there are no specific tools designed exclusively for the MacBook Pro M4 for neural surface reconstruction, the device's capabilities make it a promising platform for running such tasks with the right software adaptations.
Citations:[1] https://developer.nvidia.com/blog/recreate-high-fidelity-digital-twins-with-neural-kernel-surface-reconstruction/
[2] https://research.nvidia.com/labs/dir/neuralangelo/
[3] https://www.apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence/
[4] https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/04691.pdf
[5] https://www.xxlong.site/SparseNeuS/
[6] https://sixcolors.com/post/2024/11/m4-m4-pro-macbook-pro-review-brighter-clearer-faster/
[7] https://www.reddit.com/r/MacOS/comments/wk9cju/apple_m1_mac_instant_neural_graphics_primitives/
[8] https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.pdf
[9] https://dazinovic.github.io/neural-rgbd-surface-reconstruction/static/pdf/neural_rgbd_surface_reconstruction.pdf
[10] https://github.com/bennyguo/instant-nsr-pl