mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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0b042be956
* Model class done, pipeline class still needs more visualization stuffs * Renaming the BaseDataContainer to DataloaderOutputs and adding documentation * Pushing more documentation, removing some trivial functions * Updating dict typing * Removing config dicts for collider * Passing tests * Pushing shell new dataloader * Saving work, need to fix NGP model's get_loss_dict and Pipeline * V1 of the ngp pipeline should be done. Need train script and debugging * Updating with latest instant-ngp code * Syntax updates from latest ngp graph code * Linter, removing unneeded code in new NGP graph code * Style * Should be good now. Linter is happy and the code is verbatum current instant-ngp * Everything done except novel view testing code * Fixing some nitpicks * Debugging dataloader * merged master * WOP. integrating pipeline with configs and notebook demo * removing model folder * a lot of name changing from Graph to Model * in progress... changing trainer to use pipeline... broken right now * pipeline code runs * switched to val dataset * makes run_eval.py work * updated configs for pipeline change * fixing test_run_train script * fixing black and linting * fix docs * small updates to trainer Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Ray generator.
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"""
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import torch
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from torch import nn
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from torchtyping import TensorType
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from nerfactory.cameras.rays import RayBundle
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from nerfactory.dataloaders.structs import SceneBounds
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class SceneBoundsCollider(nn.Module):
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"""Module for setting near and far values for rays."""
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def __init__(self, **kwargs) -> None:
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self.kwargs = kwargs
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super().__init__()
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def forward(self, ray_bundle: RayBundle) -> RayBundle:
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"""To be implemented."""
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raise NotImplementedError
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class AABBBoxCollider(SceneBoundsCollider):
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"""Module for colliding rays with the scene bounds to compute near and far values."""
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def __init__(self, scene_bounds: SceneBounds, **kwargs) -> None:
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super().__init__(**kwargs)
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self.scene_bounds = scene_bounds
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@classmethod
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def intersect_with_aabb(
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cls, rays_o: TensorType["num_rays", 3], rays_d: TensorType["num_rays", 3], aabb: TensorType[2, 3]
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):
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"""_summary_
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Args:
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rays_o (torch.tensor): (num_rays, 3)
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rays_d (torch.tensor): (num_rays, 3)
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aabb (torch.tensor): (2, 3) This is [min point (x,y,z), max point (x,y,z)]
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"""
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# avoid divide by zero
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dir_fraction = 1.0 / (rays_d + 1e-6)
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# x
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t1 = (aabb[0, 0] - rays_o[:, 0:1]) * dir_fraction[:, 0:1]
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t2 = (aabb[1, 0] - rays_o[:, 0:1]) * dir_fraction[:, 0:1]
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# y
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t3 = (aabb[0, 1] - rays_o[:, 1:2]) * dir_fraction[:, 1:2]
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t4 = (aabb[1, 1] - rays_o[:, 1:2]) * dir_fraction[:, 1:2]
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# z
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t5 = (aabb[0, 2] - rays_o[:, 2:3]) * dir_fraction[:, 2:3]
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t6 = (aabb[1, 2] - rays_o[:, 2:3]) * dir_fraction[:, 2:3]
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nears = torch.max(
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torch.cat([torch.minimum(t1, t2), torch.minimum(t3, t4), torch.minimum(t5, t6)], dim=1), dim=1
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).values
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fars = torch.min(
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torch.cat([torch.maximum(t1, t2), torch.maximum(t3, t4), torch.maximum(t5, t6)], dim=1), dim=1
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).values
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# fars < 0: means the ray is behind the camera
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# nears > fars: means no intersection
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valid_mask = ((fars > nears).float() * (fars > 0).float()).bool()
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nears[nears < 0.0] = 0.0
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nears[~valid_mask] = 0.0
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fars[~valid_mask] = 0.0
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return nears, fars, valid_mask
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def forward(self, ray_bundle: RayBundle) -> RayBundle:
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"""Intersects the rays with the scene bounds and updates the near and far values.
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Populates nears and fars fields and returns the ray_bundle.
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"""
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aabb = self.scene_bounds.aabb
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nears, fars, valid_mask = self.intersect_with_aabb(ray_bundle.origins, ray_bundle.directions, aabb)
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ray_bundle.nears = nears[..., None]
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ray_bundle.fars = fars[..., None]
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ray_bundle.valid_mask = valid_mask[..., None]
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return ray_bundle
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class NearFarCollider(SceneBoundsCollider):
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"""Sets the nears and fars with fixed values."""
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def __init__(self, near_plane, far_plane, **kwargs) -> None:
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self.near_plane = near_plane
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self.far_plane = far_plane
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super().__init__(**kwargs)
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def forward(self, ray_bundle: RayBundle) -> RayBundle:
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ones = torch.ones_like(ray_bundle.origins[..., 0:1])
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ray_bundle.nears = ones * self.near_plane
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ray_bundle.fars = ones * self.far_plane
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ray_bundle.valid_mask = ones.bool()
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return ray_bundle
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