1. In `grid_encode.py`, `log2` in Python is not exactly same with `std::log2` in C++. This can lead to a mismatch in `scale` in some cases. 2. The `jt.nn.softplus` is not the same as the softplus in PyTorch. I don't know if this will cause any problems. 3. In GridEncode, JNeRF implementation doesn't have the loss scale while tcnn has it (with loss_scale=128). This will cause gradient disappearance. 4. When computing `alpha` in sdf, JNeRF uses safe_clip(0.0, 1.0). I think clamp_(0.0, 1.0) should be used. 5. The `cumprod` function in jittor is unsafe! Have a look at PyTorch implementation or mine in Jeuralangelo. 6. JNeRF doesn't have the weight norm but I found it in Jittor's source. I don't know why.
grid_encode.py,log2in Python is not exactly same withstd::log2in C++. This can lead to a mismatch inscalein some cases.jt.nn.softplusis not the same as the softplus in PyTorch. I don't know if this will cause any problems.alphain sdf, JNeRF uses safe_clip(0.0, 1.0). I think clamp_(0.0, 1.0) should be used.cumprodfunction in jittor is unsafe! Have a look at PyTorch implementation or mine in Jeuralangelo.