DeepSDFStruct.deep_sdf.networks.deep_sdf_decoder#
Standard DeepSDF Decoder Network#
This module implements the standard DeepSDF decoder architecture from Park et al. (2019). The network takes a latent code and 3D coordinates as input and outputs a signed distance value.
Architecture#
The decoder is a multi-layer perceptron (MLP) with: - Concatenation of latent code and spatial coordinates at input - Optional skip connections for latent code injection - Optional coordinate injection at multiple layers - Batch normalization or weight normalization - ReLU activations (or tanh for final layer) - Dropout for regularization
The network learns to map from a latent space to implicit SDF representations, enabling compact encoding of complex 3D geometries.
Classes
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- class DeepSDFStruct.deep_sdf.networks.deep_sdf_decoder.DeepSDFDecoder(latent_size, dims, geom_dimension, dropout=None, dropout_prob=0.0, norm_layers=(), latent_in=(), weight_norm=False, xyz_in_all=None, use_tanh=False, latent_dropout=False)#
Bases:
torch.nn.modules.module.Module- forward(input)#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.