Prerained model of SVR about bsp-net-pytorch HOT 4 CLOSED

Yes. Phase 3 provides slightly better results. But to truly improve the result quality, you may want to sample more points on the shapes (in the data pre-processing step), because the current data with at most 16384 sampled points (inside+outside) per shape is not sufficient for good reconstructions.

from bsp-net-pytorch.

Thank you for the response.
1.Does sampling more points on the shapes in the data pre-processing step mean changing the code
batch_size_3 = 16 * 16 * 16 * 4 in 2_gather_256vox_16_32_64.py in IM-NET repository?
2.I have another question. BSP-NET can generate sharp edges, so can it only generate approximate polygons for circles, as shown in Figure 7 in the paper？

from bsp-net-pytorch.

1. Yes, and also batch_size_2.

2. Yes. One solution is to use quadric surfaces to replace planes, but it may lead to overfitting. See the following papers for references.
   (a) https://arxiv.org/abs/2106.14274
   (b) https://arxiv.org/abs/2104.05652
   (c) Code of (a): https://github.com/czq142857/BSP-NET-original/tree/master/bsp_fc_qs_deeper_2048q

from bsp-net-pytorch.

Thank you for your prompt reply,it helps me a lot!I will pay attention to other work of your team.

from bsp-net-pytorch.