Summary:
Introduce methods to approximate the radii of conical frustums along rays as described in [MipNerf](https://arxiv.org/abs/2103.13415):
- Two new attributes are added to ImplicitronRayBundle: bins and radii. Bins is of size n_pts_per_ray + 1. It allows us to manipulate easily and n_pts_per_ray intervals. For example we need the intervals coordinates in the radii computation for \(t_{\mu}, t_{\delta}\). Radii are used to store the radii of the conical frustums.
- Add 3 new methods to compute the radii:
- approximate_conical_frustum_as_gaussians: It computes the mean along the ray direction, the variance of the
conical frustum with respect to t and variance of the conical frustum with respect to its radius. This
implementation follows the stable computation defined in the paper.
- compute_3d_diagonal_covariance_gaussian: Will leverage the two previously computed variances to find the
diagonal covariance of the Gaussian.
- conical_frustum_to_gaussian: Mix everything together to compute the means and the diagonal covariances along
the ray of the Gaussians.
- In AbstractMaskRaySampler, introduces the attribute `cast_ray_bundle_as_cone`. If False it won't change the previous behaviour of the RaySampler. However if True, the samplers will sample `n_pts_per_ray +1` instead of `n_pts_per_ray`. This points are then used to set the bins attribute of ImplicitronRayBundle. The support of HeterogeneousRayBundle has not been added since the current code does not allow it. A safeguard has been added to avoid a silent bug in the future.
Reviewed By: shapovalov
Differential Revision: D45269190
fbshipit-source-id: bf22fad12d71d55392f054e3f680013aa0d59b78
Summary:
Changed ray_sampler and metrics to be able to use mixed frame raysampling.
Ray_sampler now has a new member which it passes to the pytorch3d raysampler.
If the raybundle is heterogeneous metrics now samples images by padding xys first. This reduces memory consumption.
Reviewed By: bottler, kjchalup
Differential Revision: D39542221
fbshipit-source-id: a6fec23838d3049ae5c2fd2e1f641c46c7c927e3
Summary: Don't copy from one part of config to another, rather do the copy within GenericModel.
Reviewed By: davnov134
Differential Revision: D38248828
fbshipit-source-id: ff8af985c37ea1f7df9e0aa0a45a58df34c3f893
Summary:
Stats are logically connected to the training loop, not to the model. Hence, moving to the training loop.
Also removing resume_epoch from OptimizerFactory in favor of a single place - ModelFactory. This removes the need for config consistency checks etc.
Reviewed By: kjchalup
Differential Revision: D38313475
fbshipit-source-id: a1d188a63e28459df381ff98ad8acdcdb14887b7
Summary:
Make ViewMetrics easy to replace by putting them into an OmegaConf dataclass.
Also, re-word a few variable names and fix minor TODOs.
Reviewed By: bottler
Differential Revision: D37327157
fbshipit-source-id: 78d8e39bbb3548b952f10abbe05688409fb987cc
Summary: Allow specifying a color for non-opaque pixels in LSTMRenderer.
Reviewed By: davnov134
Differential Revision: D37172537
fbshipit-source-id: 6039726678bb7947f7d8cd04035b5023b2d5398c
Summary: replace dataset_zoo with a pluggable DatasetMapProvider. The logic is now in annotated_file_dataset_map_provider.
Reviewed By: shapovalov
Differential Revision: D36443965
fbshipit-source-id: 9087649802810055e150b2fbfcc3c197a761f28a
Summary: Make ResNetFeatureExtractor be an implementation of FeatureExtractorBase.
Reviewed By: davnov134
Differential Revision: D35433098
fbshipit-source-id: 0664a9166a88e150231cfe2eceba017ae55aed3a
Summary: Implements a ViewPooler that groups ViewSampler and FeatureAggregator.
Reviewed By: shapovalov
Differential Revision: D35852367
fbshipit-source-id: c1bcaf5a1f826ff94efce53aa5836121ad9c50ec
