150 lines
4.5 KiB
YAML
150 lines
4.5 KiB
YAML
data:
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background_color: [1.0, 1.0, 1.0]
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cond_width: 252 # multiply of 14
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cond_height: 252
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relative_pose: true
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num_workers: 16
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eval_batch_size: 1
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eval_height: 512
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eval_width: 512
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system:
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camera_embedder_cls: TriplaneGaussian.models.networks.MLP
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camera_embedder:
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dim_in: 25 # c2w + [fx, fy, cx, cy]
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dim_out: 768
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n_neurons: 768
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n_hidden_layers: 1
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activation: silu
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image_feature:
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out_dim: 773
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image_tokenizer_cls: TriplaneGaussian.models.tokenizers.image.DINOV2SingleImageTokenizer
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image_tokenizer:
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pretrained_model_name_or_path: "facebook/dinov2-base"
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width: ${data.cond_width}
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height: ${data.cond_height}
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modulation: true
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modulation_zero_init: true
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modulation_single_layer: true
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modulation_cond_dim: ${system.camera_embedder.dim_out} # c2w + intrinsic
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freeze_backbone_params: false
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enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention}
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enable_gradient_checkpointing: ${system.backbone.gradient_checkpointing}
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tokenizer_cls: TriplaneGaussian.models.tokenizers.triplane.TriplaneLearnablePositionalEmbedding
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tokenizer:
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plane_size: 32
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num_channels: 512
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backbone_cls: TriplaneGaussian.models.transformers.Transformer1D
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backbone:
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in_channels: ${system.tokenizer.num_channels}
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num_attention_heads: 8
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attention_head_dim: 64
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num_layers: 10
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cross_attention_dim: 768 # hard-code, =DINO feature dim
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norm_type: "layer_norm"
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enable_memory_efficient_attention: false
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gradient_checkpointing: false
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post_processor_cls: TriplaneGaussian.models.networks.TriplaneUpsampleNetwork
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post_processor:
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in_channels: ${system.tokenizer.num_channels}
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out_channels: 80
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pointcloud_generator_cls: TriplaneGaussian.models.pointclouds.simplepoint.SimplePointGenerator
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pointcloud_generator:
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camera_embedder_cls: TriplaneGaussian.models.networks.MLP
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camera_embedder:
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dim_in: 25 # c2w + [fx, fy, cx, cy]
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dim_out: 768
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n_neurons: 768
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n_hidden_layers: 1
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activation: silu
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image_tokenizer_cls: TriplaneGaussian.models.tokenizers.image.DINOV2SingleImageTokenizer
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image_tokenizer:
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pretrained_model_name_or_path: "facebook/dinov2-base"
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width: ${data.cond_width}
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height: ${data.cond_height}
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modulation: true
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modulation_zero_init: true
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modulation_single_layer: true
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modulation_cond_dim: ${system.camera_embedder.dim_out} # c2w + intrinsic
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freeze_backbone_params: true
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enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention}
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enable_gradient_checkpointing: false
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tokenizer_cls: TriplaneGaussian.models.tokenizers.point.PointLearnablePositionalEmbedding
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tokenizer:
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num_pcl: 2048
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num_channels: 512
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backbone_cls: TriplaneGaussian.models.transformers.Transformer1D
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backbone:
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in_channels: ${system.pointcloud_generator.tokenizer.num_channels}
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num_attention_heads: 8
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attention_head_dim: 64
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num_layers: 10
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cross_attention_dim: 768 # hard-code, =DINO feature dim
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norm_type: "layer_norm"
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enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention}
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gradient_checkpointing: ${system.backbone.gradient_checkpointing}
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post_processor_cls: TriplaneGaussian.models.networks.PointOutLayer
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post_processor:
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in_channels: 512
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out_channels: 3
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pointcloud_upsampling_cls: TriplaneGaussian.models.snowflake.model_spdpp.SnowflakeModelSPDPP
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pointcloud_upsampling:
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input_channels: 768
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dim_feat: 128
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num_p0: 2048
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radius: 1
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bounding: true
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use_fps: true
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up_factors: [2,4]
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token_type: "image_token"
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pointcloud_encoder_cls: TriplaneGaussian.models.pointclouds.pointnet.LocalPoolPointnet
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pointcloud_encoder:
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input_channels: 776 # 3 + 3 + 768 + 1 + 1 [xyz, local features]
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c_dim: ${system.tokenizer.num_channels}
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hidden_dim: 128
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plane_size: ${system.tokenizer.plane_size}
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n_blocks: 5
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radius: ${system.renderer.radius}
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renderer_cls: TriplaneGaussian.models.renderer.GS3DRenderer
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renderer:
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sh_degree: 3
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radius: 0.6
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mlp_network_config:
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n_neurons: ${system.renderer.gs_out.in_channels}
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n_hidden_layers: 2
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activation: silu
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gs_out:
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in_channels: 128
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xyz_offset: true
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restrict_offset: true
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use_rgb: false
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feature_channels:
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xyz: 3
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scaling: 3
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rotation: 4
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opacity: 1
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shs: ${shsdim:${system.renderer.sh_degree}}
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clip_scaling: 0.2 |