# Copyright 2022 The Nerfstudio Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Put all the method implementations in one location. """ from __future__ import annotations from typing import Dict import tyro from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig from nerfstudio.configs.base_config import ( Config, SchedulerConfig, TrainerConfig, ViewerConfig, ) from nerfstudio.data.datamanagers.base_datamanager import ( FlexibleDataManagerConfig, VanillaDataManagerConfig, ) from nerfstudio.data.datamanagers.semantic_datamanager import SemanticDataManagerConfig from nerfstudio.data.datamanagers.variable_res_datamanager import ( VariableResDataManagerConfig, ) from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig from nerfstudio.data.dataparsers.friends_dataparser import FriendsDataParserConfig from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig from nerfstudio.data.dataparsers.phototourism_dataparser import ( PhototourismDataParserConfig, ) from nerfstudio.data.dataparsers.sdfstudio_dataparser import SDFStudioDataParserConfig from nerfstudio.engine.optimizers import AdamOptimizerConfig, RAdamOptimizerConfig, AdamWOptimizerConfig from nerfstudio.engine.schedulers import ( ExponentialSchedulerConfig, MultiStepSchedulerConfig, NeuSSchedulerConfig, MultiStepWarmupSchedulerConfig, ) from nerfstudio.field_components.temporal_distortions import TemporalDistortionKind from nerfstudio.fields.sdf_field import SDFFieldConfig from nerfstudio.models.bakedsdf import BakedSDFModelConfig from nerfstudio.models.dto import DtoOModelConfig from nerfstudio.models.instant_ngp import InstantNGPModelConfig from nerfstudio.models.mipnerf import MipNerfModel from nerfstudio.models.nerfacto import NerfactoModelConfig from nerfstudio.models.neuralangelo import NeuralangeloModelConfig from nerfstudio.models.bakedangelo import BakedAngeloModelConfig from nerfstudio.models.neuralreconW import NeuralReconWModelConfig from nerfstudio.models.neus import NeuSModelConfig from nerfstudio.models.neus_acc import NeuSAccModelConfig from nerfstudio.models.neus_facto import NeuSFactoModelConfig from nerfstudio.models.semantic_nerfw import SemanticNerfWModelConfig from nerfstudio.models.tensorf import TensoRFModelConfig from nerfstudio.models.unisurf import UniSurfModelConfig from nerfstudio.models.vanilla_nerf import NeRFModel, VanillaModelConfig from nerfstudio.models.volsdf import VolSDFModelConfig from nerfstudio.pipelines.base_pipeline import ( FlexibleInputPipelineConfig, VanillaPipelineConfig, ) from nerfstudio.pipelines.dynamic_batch import DynamicBatchPipelineConfig method_configs: Dict[str, Config] = {} descriptions = { "nerfacto": "Recommended real-time model tuned for real captures. This model will be continually updated.", "instant-ngp": "Implementation of Instant-NGP. Recommended real-time model for bounded synthetic data.", "mipnerf": "High quality model for bounded scenes. (slow)", "semantic-nerfw": "Predicts semantic segmentations and filters out transient objects.", "vanilla-nerf": "Original NeRF model. (slow)", "volsdf": "Implementation of VolSDF.", "monosdf": "Implementation of MonoSDF.", "geo-volsdf": "Implementation of patch warping from GeoNeuS with VolSDF.", "neus": "Implementation of NeuS.", "mono-neus": "Implementation of MonoSDF with NeuS rendering formulation.", "geo-neus": "Implementation of patch warping from GeoNeuS with NeuS.", "unisurf": "Implementation of UniSurf.", "mono-unisurf": "Implementation of MonoSDF with unisurf rendering formulation.", "geo-unisurf": "Implementation of patch warping from GeoNeuS with UniSurf.", "tensorf": "tensorf", "dnerf": "Dynamic-NeRF model. (slow)", "phototourism": "Uses the Phototourism data.", "dto": "Occupancy field with density guided sampling", "neusW": "Implementation of Neural Reconstruction in the wild", "neus-acc": "Implementation of NeuS with empty space skipping.", "neus-facto": "Implementation of NeuS similar to nerfacto where proposal sampler is used.", "neus-facto-bigmlp": "NeuS-facto with big MLP, it is used in training heritage data with 8 gpus", "bakedsdf": "Implementation of BakedSDF with multi-res hash grids", "bakedsdf-mlp": "Implementation of BakedSDF with large MLPs", "neuralangelo": "Implementation of Neuralangelo", "bakedangelo": "Implementation of Neuralangelo with BakedSDF", "neus-facto-angelo": "Implementation of Neuralangelo with neus-facto", } method_configs["bakedangelo"] = Config( method_name="bakedangelo", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=1000_001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=8192, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=BakedAngeloModelConfig( near_plane=0.01, far_plane=1000.0, overwrite_near_far_plane=True, sdf_field=SDFFieldConfig( use_grid_feature=True, num_layers=1, num_layers_color=4, hidden_dim=256, hidden_dim_color=256, geometric_init=True, bias=1.5, beta_init=0.1, inside_outside=True, use_appearance_embedding=True, use_numerical_gradients=True, base_res=64, max_res=4096, log2_hashmap_size=22, hash_features_per_level=8, hash_smoothstep=False, use_position_encoding=False, ), eikonal_loss_mult=0.01, background_model="grid", proposal_weights_anneal_max_num_iters=10000, use_anneal_beta=True, eval_num_rays_per_chunk=1024, use_spatial_varying_eikonal_loss=False, steps_per_level=10_000, curvature_loss_warmup_steps=20_000, beta_anneal_end=0.0002, beta_anneal_max_num_iters=1000_000, ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=1000_000), }, "fields": { "optimizer": AdamWOptimizerConfig(lr=1e-3, eps=1e-15, weight_decay=1e-2), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[600_000, 800_000], gamma=0.1), }, "field_background": { "optimizer": AdamWOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[300_000, 400_000], gamma=0.1), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neuralangelo"] = Config( method_name="neuralangelo", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=500_001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=512, eval_num_rays_per_batch=512, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuralangeloModelConfig( sdf_field=SDFFieldConfig( use_grid_feature=True, num_layers=1, num_layers_color=4, hidden_dim=256, hidden_dim_color=256, geometric_init=True, bias=0.5, beta_init=0.3, inside_outside=False, use_appearance_embedding=False, position_encoding_max_degree=6, use_numerical_gradients=True, base_res=64, max_res=4096, log2_hashmap_size=22, hash_features_per_level=8, hash_smoothstep=False, use_position_encoding=False, ), background_model="mlp", enable_progressive_hash_encoding=True, enable_curvature_loss_schedule=True, enable_numerical_gradients_schedule=True, ), ), optimizers={ "fields": { "optimizer": AdamWOptimizerConfig(lr=1e-3, weight_decay=0.01, eps=1e-15), # "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=500000), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[300_000, 400_000], gamma=0.1), }, "field_background": { "optimizer": AdamWOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[300_000, 400_000], gamma=0.1), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["bakedsdf"] = Config( method_name="bakedsdf", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=250001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=8192, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=BakedSDFModelConfig( near_plane=0.2, far_plane=1000.0, overwrite_near_far_plane=True, sdf_field=SDFFieldConfig( use_grid_feature=True, num_layers=2, num_layers_color=2, hidden_dim=256, hidden_dim_color=256, geometric_init=True, bias=0.05, beta_init=0.1, inside_outside=False, use_appearance_embedding=False, position_encoding_max_degree=8, use_diffuse_color=True, use_specular_tint=True, use_reflections=True, use_n_dot_v=True, off_axis=True, ), eikonal_loss_mult=0.01, background_model="none", proposal_weights_anneal_max_num_iters=1000, use_anneal_beta=True, eval_num_rays_per_chunk=1024, ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=250000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=250000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=250000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["bakedsdf-mlp"] = Config( method_name="bakedsdf-mlp", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=250001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=4096, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=BakedSDFModelConfig( near_plane=0.2, far_plane=1000.0, overwrite_near_far_plane=True, sdf_field=SDFFieldConfig( use_grid_feature=False, num_layers=8, num_layers_color=2, hidden_dim=1024, hidden_dim_color=256, geometric_init=True, bias=0.05, beta_init=0.1, inside_outside=False, use_appearance_embedding=False, position_encoding_max_degree=8, use_diffuse_color=True, use_specular_tint=True, use_reflections=True, use_n_dot_v=True, off_axis=True, ), eikonal_loss_mult=0.01, background_model="none", use_anneal_beta=True, proposal_weights_anneal_max_num_iters=20000, eval_num_rays_per_chunk=1024, use_spatial_varying_eikonal_loss=True, ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=250000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=0.002, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=250000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=250000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neus-facto-angelo"] = Config( method_name="neus-facto-angelo", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=1000_001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSFactoModelConfig( near_plane=0.01, far_plane=1000.0, overwrite_near_far_plane=True, sdf_field=SDFFieldConfig( use_grid_feature=True, num_layers=1, num_layers_color=4, hidden_dim=256, hidden_dim_color=256, geometric_init=True, bias=0.5, beta_init=0.3, inside_outside=False, use_appearance_embedding=True, use_numerical_gradients=True, base_res=64, max_res=4096, log2_hashmap_size=22, hash_features_per_level=8, hash_smoothstep=False, use_position_encoding=False, ), background_model="grid", eval_num_rays_per_chunk=1024, level_init=8, eikonal_loss_mult=0.01, use_anneal_beta=True, enable_progressive_hash_encoding=True, enable_numerical_gradients_schedule=True, enable_curvature_loss_schedule=True, curvature_loss_multi=5e-4, ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=1000_000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[600_000, 800_000], gamma=0.1), }, "field_background": { "optimizer": AdamWOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": MultiStepWarmupSchedulerConfig(warm_up_end=5000, milestones=[300_000, 400_000], gamma=0.1), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neus-facto"] = Config( method_name="neus-facto", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=20001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSFactoModelConfig( sdf_field=SDFFieldConfig( use_grid_feature=True, num_layers=2, num_layers_color=2, hidden_dim=256, bias=0.5, beta_init=0.3, use_appearance_embedding=False, ), background_model="none", eval_num_rays_per_chunk=1024, ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=20000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neus-facto-bigmlp"] = Config( method_name="neus-facto-bigmlp", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100001, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSFactoModelConfig( sdf_field=SDFFieldConfig(num_layers=8, hidden_dim=512, num_layers_color=4), eval_num_rays_per_chunk=1024 ), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=100000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=100000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=100000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["geo-volsdf"] = Config( method_name="geo-volsdf", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=200001, mixed_precision=False, ), pipeline=FlexibleInputPipelineConfig( datamanager=FlexibleDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=VolSDFModelConfig(patch_warp_loss_mult=0.1, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": MultiStepSchedulerConfig( max_steps=1000000 ), # set max_steps to a large value so it never step and we will use the last_lr form the pretrained model }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": ExponentialSchedulerConfig(decay_rate=0.1, max_steps=200000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["monosdf"] = Config( method_name="monosdf", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=200000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=VolSDFModelConfig(mono_depth_loss_mult=0.1, mono_normal_loss_mult=0.05, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": ExponentialSchedulerConfig(decay_rate=0.1, max_steps=200000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": ExponentialSchedulerConfig(decay_rate=0.1, max_steps=200000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["volsdf"] = Config( method_name="volsdf", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=VolSDFModelConfig(eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": ExponentialSchedulerConfig(decay_rate=0.1, max_steps=100000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": ExponentialSchedulerConfig(decay_rate=0.1, max_steps=100000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["geo-neus"] = Config( method_name="geo-neus", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=200000, mixed_precision=False, ), pipeline=FlexibleInputPipelineConfig( datamanager=FlexibleDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSModelConfig(patch_warp_loss_mult=0.1, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["mono-neus"] = Config( method_name="mono-neus", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSModelConfig(mono_depth_loss_mult=0.1, mono_normal_loss_mult=0.05, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neus"] = Config( method_name="neus", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSModelConfig(eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["unisurf"] = Config( method_name="unisurf", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=UniSurfModelConfig(eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["mono-unisurf"] = Config( method_name="mono-unisurf", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=UniSurfModelConfig(mono_depth_loss_mult=0.1, mono_normal_loss_mult=0.05, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["geo-unisurf"] = Config( method_name="geo-unisurf", trainer=TrainerConfig( steps_per_eval_image=500, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=FlexibleInputPipelineConfig( datamanager=FlexibleDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=1024, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=UniSurfModelConfig(patch_warp_loss_mult=0.1, eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["dto"] = Config( method_name="dto", trainer=TrainerConfig( steps_per_eval_image=2000, steps_per_eval_batch=5000, steps_per_save=5000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=2048, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=DtoOModelConfig(eval_num_rays_per_chunk=1 << 10), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=300000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=300000), }, "occupancy_field": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["neusW"] = Config( method_name="neusW", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=5000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=100000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=2048, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuralReconWModelConfig( background_model="grid", num_samples_outside=4, eikonal_loss_mult=0.0001, eval_num_rays_per_chunk=1 << 10 ), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=300000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="wandb", ) method_configs["neus-acc"] = Config( method_name="neus-acc", trainer=TrainerConfig( steps_per_eval_image=5000, steps_per_eval_batch=5000, steps_per_save=20000, steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images max_num_iterations=20000, mixed_precision=False, ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=SDFStudioDataParserConfig(), train_num_rays_per_batch=2048, eval_num_rays_per_batch=1024, camera_optimizer=CameraOptimizerConfig( mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NeuSAccModelConfig(eval_num_rays_per_chunk=1024), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20000), }, "field_background": { "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15), "scheduler": NeuSSchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["nerfacto"] = Config( method_name="nerfacto", trainer=TrainerConfig( steps_per_eval_batch=5000, steps_per_save=2000, max_num_iterations=30000, mixed_precision=True ), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=4096, eval_num_rays_per_batch=4096, camera_optimizer=CameraOptimizerConfig( mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 15), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=300000), }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=300000), }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) method_configs["instant-ngp"] = Config( method_name="instant-ngp", trainer=TrainerConfig( steps_per_eval_batch=5000, steps_per_eval_image=5000, steps_per_save=20000, max_num_iterations=20001, mixed_precision=True, steps_per_eval_all_images=20000, ), pipeline=DynamicBatchPipelineConfig( datamanager=VanillaDataManagerConfig(dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=8192), model=InstantNGPModelConfig(render_step_size=0.005, eval_num_rays_per_chunk=8192), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": MultiStepSchedulerConfig(max_steps=20000), } }, viewer=ViewerConfig(num_rays_per_chunk=64000), vis="viewer", ) method_configs["mipnerf"] = Config( method_name="mipnerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig(dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=1024), model=VanillaModelConfig( _target=MipNerfModel, loss_coefficients={"rgb_loss_coarse": 0.1, "rgb_loss_fine": 1.0}, num_coarse_samples=128, num_importance_samples=128, eval_num_rays_per_chunk=1024, ), ), optimizers={ "fields": { "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08), "scheduler": None, } }, ) method_configs["semantic-nerfw"] = Config( method_name="semantic-nerfw", trainer=TrainerConfig( steps_per_eval_batch=500, steps_per_save=2000, max_num_iterations=30000, mixed_precision=True ), pipeline=VanillaPipelineConfig( datamanager=SemanticDataManagerConfig( dataparser=FriendsDataParserConfig(), train_num_rays_per_batch=4096, eval_num_rays_per_batch=8192 ), model=SemanticNerfWModelConfig(eval_num_rays_per_chunk=1 << 16), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": None, }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": None, }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 16), vis="viewer", ) method_configs["vanilla-nerf"] = Config( method_name="vanilla-nerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=BlenderDataParserConfig(), ), model=VanillaModelConfig(_target=NeRFModel), ), optimizers={ "fields": { "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08), "scheduler": None, }, "temporal_distortion": { "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08), "scheduler": None, }, }, ) method_configs["tensorf"] = Config( method_name="tensorf", trainer=TrainerConfig(mixed_precision=False), pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( dataparser=BlenderDataParserConfig(), ), model=TensoRFModelConfig(), ), optimizers={ "fields": { "optimizer": AdamOptimizerConfig(lr=0.001), "scheduler": SchedulerConfig(lr_final=0.0001, max_steps=30000), }, "encodings": { "optimizer": AdamOptimizerConfig(lr=0.02), "scheduler": SchedulerConfig(lr_final=0.002, max_steps=30000), }, }, ) method_configs["dnerf"] = Config( method_name="dnerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig(dataparser=DNeRFDataParserConfig()), model=VanillaModelConfig( _target=NeRFModel, enable_temporal_distortion=True, temporal_distortion_params={"kind": TemporalDistortionKind.DNERF}, ), ), optimizers={ "fields": { "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08), "scheduler": None, }, "temporal_distortion": { "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08), "scheduler": None, }, }, ) method_configs["phototourism"] = Config( method_name="phototourism", trainer=TrainerConfig( steps_per_eval_batch=500, steps_per_save=2000, max_num_iterations=30000, mixed_precision=True ), pipeline=VanillaPipelineConfig( datamanager=VariableResDataManagerConfig( # NOTE: one of the only differences with nerfacto dataparser=PhototourismDataParserConfig(), # NOTE: one of the only differences with nerfacto train_num_rays_per_batch=4096, eval_num_rays_per_batch=4096, camera_optimizer=CameraOptimizerConfig( mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2) ), ), model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 15), ), optimizers={ "proposal_networks": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": None, }, "fields": { "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), "scheduler": None, }, }, viewer=ViewerConfig(num_rays_per_chunk=1 << 15), vis="viewer", ) AnnotatedBaseConfigUnion = tyro.conf.SuppressFixed[ # Don't show unparseable (fixed) arguments in helptext. tyro.conf.FlagConversionOff[ tyro.extras.subcommand_type_from_defaults(defaults=method_configs, descriptions=descriptions) ] ] """Union[] type over config types, annotated with default instances for use with tyro.cli(). Allows the user to pick between one of several base configurations, and then override values in it."""