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Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
In main.cpp: ``` for (int i = 0; i < pnumber; i++) { fscanf(pointreader, "%lf %lf %lf", &po[i].xyz[0], &po[i].xyz[1], &po[i].xyz[2]); int ppp = floor(((po[i].xyz[0] + 0.5) / cell)) * boxsize...
Thank you for your code. I have a question about "g++ -std=c++11 dense.cpp -O2 -o dense". I take this command, getting a dense file. Then I take "./dense" to generate...
=> Loading checkpoint from local file... out/fd\model_best.pt => Loading checkpoint from local file... processing test/cow.xyz generate seedpoint ./dense 0.004 2048 '.' 不是内部或外部命令，也不是可运行的程序 或批处理文件。 Traceback (most recent call last): File "generate.py",...
Thank you very much for your well organized code. May I ask how the KITTI experiment was conducted? Do you divide KITTI into multiple blocks, then perform upsampling separately?
Dear Author, thank you for the nice work. I really want to know that did you train on the PU-GAN dataset? Is there any evalution results about this? Thank you...
Hello!! Here is a question . Your traing dataset save in baidu disk. I can't download it cause i don't have baidu account. Could you put it on google drive...
Hi Zhao, Thank you so much for your excellent work Is the **output** in def compute_loss() the GT label of the dataset? Say n here is the occupancy probabilities from...
Hi, thank you so much for your great work. When run trainfn.py, there is an error saying no fn.npz files. I was wondering if we should run the scripts sh...