jingsenzhu / indoorinverserendering Goto Github PK
View Code? Open in Web Editor NEW[SIGGRAPH Asia'22] Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing
License: MIT License
[SIGGRAPH Asia'22] Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing
License: MIT License
Hi. Thanks for sharing your wonderful research. Do you not share the training part of lightNet? I want to do a comparison on different datasets.
And when will the InteriorVerse Lighting dataset be released?
thank you
Hi there, during the ray tracing process, when you consider multiple light rays for each pixels, let's say S
, each ray requires multiple samples from NeRF, let's say N
. For each image with size H
times W
, the input batch size of NeRF is S*N*H*W
. When (S, N, H, W)=(32, 64, 640, 480)
, the input batch size is nearly 6.3*10^8
, which is a huge cost of memory during NeRF's forward pass, since NeRF stores the partial derivatives of input to its weights. The storage cost is around 30~40GB
, which is usually much larger than a GPU's RAM. May I ask how do you solve the problem? Thank you.
Hello,
I am unable to load the images using your provided code. The best I get using imageio results in a very dark image. Can you help with me this pls?
Hi, I am a undergraduate student from National Taiwan University. We are looking for high quality inverse rendering datasets for our research. May I have an access to your dataset?
Hello. Thank you very much for the wonderful paper!
Will scenes be available in 3D formats, such as obj, fbx, glb, etc.?
When do you plan to release the dataset?
Hi there, great work with the paper and the dataset! I wonder if there is any plan for full release of the pretrained models and the code including the lighting part?
I have applied for the download permission of the complete data set and sent you an email. Could you please check it? My email is [email protected],Thanks !
Hello, thank you for this great paper. When will you release the spatially-varying lighting dataset?
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