DeepSDFStruct.deep_sdf.networks.hierarchical_deep_sdf_decoder#
Hierarchical DeepSDF Decoder Network#
This module implements a hierarchical decoder architecture that injects latent codes at multiple layers rather than only at the input. This enables multi-scale geometric control and better representation of complex features.
Architecture#
Unlike the standard DeepSDF decoder, this architecture: - Accepts multiple latent vectors of potentially different sizes - Injects each latent at a specified layer depth - Enables hierarchical control from coarse to fine details - Supports various activation functions (ReLU, GELU, SiLU, etc.)
The hierarchical structure is particularly useful for: - Multi-resolution geometric features - Compositional shape generation - Transfer learning across scales - Fine-tuning specific detail levels
This architecture is inspired by progressive training and multi-scale representations in generative models.
Classes
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- class DeepSDFStruct.deep_sdf.networks.hierarchical_deep_sdf_decoder.HierachicalDeepSDFDecoder(latent_size, dims, geom_dimension, dropout=None, dropout_prob=0.0, latent_in=[], weight_norm=False, norm_layers=[], xyz_in_all=None, activation_fun='relu', 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.