Pixel-Perfect Structure-from-Motion
Best student paper award @ ICCV 2021
We introduce a framework that improves the accuracy of Structure-from-Motion (SfM) and visual localization by refining keypoints, camera poses, and 3D points using the direct alignment of deep features. It is presented in our paper:
- Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
- Authors: Philipp Lindenberger*, Paul-Edouard Sarlin*, Viktor Larsson, and Marc Pollefeys
- Website: psarlin.com/pixsfm (videos, slides, poster)
Here we provide pixsfm, a Python package that can be readily used with COLMAP and our toolbox hloc. This makes it easy to refine an existing COLMAP model or reconstruct a new dataset with state-of-the-art image matching. Our framework also improves visual localization in challenging conditions.
The refinement is composed of 2 steps:
- Keypoint adjustment: before SfM, jointly refine all 2D keypoints that are matched together.
- Bundle adjustment: after SfM, refine 3D points and camera poses.
In each step, we optimize the consistency of dense deep features over multiple views by minimizing a featuremetric cost. These features are extracted beforehand from the images using a pre-trained CNN.
With pixsfm, you can:
- reconstruct and refine a scene using hloc, from scratch or with given camera poses
- localize and refine new query images using hloc
- run the keypoint or bundle adjustments on a COLMAP database or 3D model
- evaluate the refinement with new dense or sparse features on the ETH3D dataset
Our implementation scales to large scenes by carefully managing the memory and leveraging parallelism and SIMD vectorization when possible.
Installation
pixsfm requires Python >=3.6, GCC >=6.1, and COLMAP installed from source (branch master). The core optimization is implemented in C++ with Ceres >= 2.1 but we provide Python bindings with high granularity. The code is written for UNIX and has not been tested on Windows. The remaining dependencies are listed in requirements.txt and include PyTorch >=1.7 and pycolmap + pyceres built from source:
# install COLMAP following colmap.github.io/install.html#build-from-source, using the master branch
sudo apt-get install libhdf5-dev
git clone https://github.com/cvg/pixel-perfect-sfm --recursive
cd pixel-perfect-sfm
pip install -r requirements.txtTo use other local features besides SIFT via COLMAP, we also require hloc:
git clone --recursive https://github.com/cvg/Hierarchical-Localization/
cd Hierarchical-Localization/
pip install -e .Finally build and install the pixsfm package:
pip install -e . # install pixsfm in develop modeWe highly recommend to use pixsfm with a working GPU for the dense feature extraction. All other steps can only run on the CPU. Having issues with compilation errors or runtime crashes? Want to use the codebase as a C++ library? Check our FAQ.
Tutorial
The Jupyter notebook demo.ipynb demonstrates a minimal usage example. It shows how to run Structure-from-Motion and the refinement, how to align and compare different 3D models, and how to localize and refine additional query images.
Visualizing mapping and localization results in the demo.
Structure-from-Motion
End-to-end SfM with hloc
Given keypoints and matches computed with hloc and stored in HDF5 files, we can run Pixel-Perfect SfM from a Python script:
from pixsfm.refine_hloc import PixSfM
refiner = PixSfM()
model, debug_outputs = refiner.reconstruction(
path_to_working_directory,
path_to_image_dir,
path_to_list_of_image_pairs,
path_to_keypoints.h5,
path_to_matches.h5,
)
# model is a pycolmap.Reconstruction 3D modelor from the command line:
python -m pixsfm.refine_hloc reconstructor \
--sfm_dir path_to_working_directory \
--image_dir path_to_image_dir \
--pairs_path path_to_list_of_image_pairs \
--features_path path_to_keypoints.h5 \
--matches_path path_to_matches.h5Note that:
- The final refined 3D model is written to
path_to_working_directoryin either case. - Dense features are automatically extracted (on GPU when available) using a pre-trained CNN, S2DNet by default.
- The result
debug_outputscontains the dense features and optimization statistics.
Configurations
We have fine-grained control over all hyperparameters via OmegaConf configurations, which have sensible default values defined in PixSfM.default_conf. See Detailed configuration for a description of the main configuration entries and their defaults.
[Click to see some examples]
For example, dense features are stored in memory by default. If we reconstruct a large scene or have limited RAM, we should instead write them to a cache file that is loaded on-demand. With the Python API, we can pass a configuration update:
refiner = PixSfM(conf={"dense_features": {"use_cache": True}})or equivalently with the command line using a dotlist:
python -m pixsfm.refine_hloc reconstructor [...] dense_features.use_cache=trueWe also provide ready-to-use configuration templates in pixsfm/configs/ covering the main use cases. For example, pixsfm/configs/low_memory.yaml reduces the memory consumption to scale to large scene and can be used as follow:
refiner = PixSfM(conf="low_memory")
# or
python -m pixsfm.refine_hloc reconstructor [...] --config low_memoryTriangulation from known camera poses
[Click to expand]
If camera poses are available, we can simply triangulate a 3D point cloud from an existing reference COLMAP model with:
model, _ = refiner.triangulation(..., path_to_reference_model, ...)or
python -m pixsfm.refine_hloc triangulator [...] \
--reference_sfm_model path_to_reference_modelBy default, camera poses and intrinsics are optimized by the bundle adjustment. To keep them fixed, we can simply overwrite the corresponding options as:
conf = {"BA": {"optimizer": {
"refine_focal_length": False,
"refine_extra_params": False, # distortion parameters
"refine_extrinsics": False, # camera poses
}}}
refiner = PixSfM(conf=conf)
refiner.triangulation(...)or equivalently
python -m pixsfm.refine_hloc triangulator [...] \
'BA.optimizer={refine_focal_length: false, refine_extra_params: false, refine_extrinsics: false}'Keypoint adjustment
The first step of the refinement is the keypoint adjustment (KA). It refines the keypoints from tentative matches only, before SfM. Here we show how to run this step separately.
[Click to expand]
To refine keypoints stored in an hloc HDF5 feature file:
from pixsfm.refine_hloc import PixSfM
refiner = PixSfM()
keypoints, _, _ = refiner.refine_keypoints(
path_to_output_keypoints.h5,
path_to_input_keypoints.h5,
path_to_list_of_image_pairs,
path_to_matches.h5,
path_to_image_dir,
)To refine keypoints stored in a COLMAP database:
from pixsfm.refine_colmap import PixSfM
refiner = PixSfM()
keypoints, _, _ = refiner.refine_keypoints_from_db(
path_to_output_database, # pass path_to_input_database for in-place refinement
path_to_input_database,
path_to_image_dir,
)In either case, there is an equivalent command line interface.
Bundle adjustment
The second contribution of the refinement is the bundle adjustment (BA). Here we show how to run it separately to refine an existing COLMAP 3D model.
[Click to expand]
To refine a 3D model stored on file:
from pixsfm.refine_colmap import PixSfM
refiner = PixSfM()
model, _, _, = refiner.refine_reconstruction(
path_to_input_model,
path_to_output_model,
path_to_image_dir,
)Using the command line interface:
python -m pixsfm.refine_colmap bundle_adjuster \
--input_path path_to_input_model \
--output_path path_to_output_model \
--image_dir path_to_image_dirVisual localization
When estimating the camera pose of a single image, we can also run the keypoint and bundle adjustments before and after PnP+RANSAC. This requires reference features attached to each observation of the reference model. They can be computed in several ways.
[Click to learn how to localize a single image]
- To recompute the references from scratch, pass the path to the reference images:
from pixsfm.localization import QueryLocalizer
localizer = QueryLocalizer(
reference_model, # pycolmap.Reconstruction 3D model
image_dir=path_to_reference_image_dir,
dense_features=cache_path, # optional: cache to file for later reuse
)
pose_dict = localizer.localize(
pnp_points2D # keypoints with valid 3D correspondence (N, 2)
pnp_point3D_ids, # IDs of corresponding 3D points in the reconstruction
query_camera, # pycolmap.Camera
image_path=path_to_query_image,
)
if pose_dict["success"]:
# quaternion and translation of the query, from world to camera
qvec, tvec = pose_dict["qvec"], pose_dict["tvec"]The default localization configuration can be accessed with QueryLocalizer.default_conf.
- Alternatively, if dense reference features have already been computed during the pixel-perfect SfM, it is more efficient to reuse them:
refiner = PixSfM()
model, outputs = refiner.reconstruction(...)
features = outputs["feature_manager"]
# or load the features manually
features = pixsfm.extract.load_features_from_cache(
refiner.resolve_cache_path(output_dir=path_to_output_sfm)
)
localizer = QueryLocalizer(
reference_model, # pycolmap.Reconstruction 3D model
dense_features=features,
)We can also batch-localize multiple queries equivalently to hloc.localize_sfm:
pixsfm.localize.main(
dense_features, # FeatureManager or path to cache file
reference_model, # pycolmap.Reconstruction 3D model
path_to_query_list,
path_to_image_dir,
path_to_image_pairs,
path_to_keypoints,
path_to_matches,
path_to_output_results,
config=config, # optional dict
)Example: mapping and localization
We now show how to run the featuremetric pipeline on the Aachen Day-Night v1.1 dataset. First, download the dataset by following the instructions described here. Then run python examples/sfm+loc_aachen.py, which will perform mapping and localization with SuperPoint+SuperGlue. As the scene is large, with over 7k images, we cache the dense feature patches and therefore require about 350GB of free disk space. Expect the sparse feature matching to take a few hours on a recent GPU. We also show in examples/refine_sift_aachen.py how to start from an existing COLMAP database.
Evaluation
We can evaluate the accuracy of the pixel-perfect SfM and of camera pose estimation on the ETH3D dataset. Refer to the paper for more details.
First, we download the dataset with python -m pixsfm.eval.eth3d.download, by default to ./datasets/ETH3D/.
3D triangulation
[Click to expand]
We first need to install the ETH3D multi-view evaluation tool:
sudo apt install libpcl-dev # linux only
git clone [email protected]:ETH3D/multi-view-evaluation.git
cd multi-view-evaluation && mkdir build && cd build
cmake .. && make -jWe can then evaluate the accuracy of the sparse 3D point cloud triangulated with Pixel-Perfect SfM, for example on the courtyard scene with SuperPoint keypoints:
python -m pixsfm.eval.eth3d.triangulation \
--scenes courtyard \
--methods superpoint \
--tag pixsfm- omit
--scenesand--methodsto run all scenes with all feature detectors. - the results are written to
./outputs/ETH3D/by default - use
--tag some_run_nameto distinguish different runs - add
--config norefineto turn off any refinement or use the dotlistKA.apply=false BA.apply=false - add
--config photometricto run the photometric BA (no KA)
To aggregate the results and compare different runs, for example with and without refinement, we run:
python -m pixsfm.eval.eth3d.plot_triangulation \
--scenes courtyard \
--methods superpoint \
--tags pixsfm rawRunning on all scenes and all detectors should yield the following results (±1%):
----scene---- -keypoints- -tag-- -accuracy @ X cm- completeness @ X cm
1.0 2.0 5.0 1.0 2.0 5.0
----------------------------------------------------------------------
indoor sift raw 75.95 85.50 92.88 0.21 0.88 3.65
pixsfm 83.16 89.94 94.94 0.25 0.96 3.77
superpoint raw 78.96 87.77 94.55 0.64 2.36 9.39
pixsfm 89.93 94.09 97.04 0.76 2.62 9.85
r2d2 raw 67.91 80.25 90.45 0.55 2.12 8.85
pixsfm 81.09 87.78 93.41 0.67 2.32 9.04
----------------------------------------------------------------------
outdoor sift raw 57.70 72.90 86.41 0.06 0.34 2.46
pixsfm 68.10 80.57 91.59 0.08 0.42 2.75
superpoint raw 53.63 68.93 83.27 0.11 0.64 4.43
pixsfm 71.83 82.65 92.06 0.18 0.89 5.40
r2d2 raw 49.33 66.21 83.37 0.11 0.55 3.62
pixsfm 67.94 81.02 91.68 0.16 0.71 3.99
The results of this evaluation can be different from the numbers reported in the paper. The trends are however similar and the conclusions of the paper still hold. This difference is due to improvements of the pixsfm code and to changes in the SuperPoint implementation: we initially used the setup of PatchFlow and later switched to hloc, which is strictly better and easier to install.
Camera pose estimation
[Click to expand]
Similarly, we evaluate the accuracy of camera pose estimation given sparse 3D models triangulated from other views:
python -m pixsfm.eval.eth3d.localization --tag pixsfmAgain, we can also run on a subset of scenes or keypoint detectors. To aggregate the results and compare different runs, for example with and without KA and BA, we run:
python -m pixsfm.eval.eth3d.plot_localization --tags pixsfm rawWe should then obtain the following table and plot (±2%):
|
SIFT (black), SuperPoint (red), R2D2 (green) |
Results for the 0.1cm threshold can vary across setups and therefore differ from the numbers reported in the paper. This might be due to changes in the PyTorch and COLMAP dependencies. We are investigating this but any help is welcome!
Advanced usage
Detailed configuration
Here we explain the main configuration entries for mapping and localization along with their default values:
[Click to expand]
dense_features: # refinement features
model: # the CNN that extracts the features
name: s2dnet # the name of one of the models defined in pixsfm/features/models/
num_layers: 1 # the number of output layers (model-specific parameters)
device: auto # cpu, cuda, or auto-determined based on CUDA availability
max_edge: 1600 # downscale the image such the largest dimension has this value
resize: LANCZOS # interpolation algorithm for the image resizing
pyr_scales: [1.0] # concat features extracted at multiple scales
fast_image_load: false # approximate resizing for large images
l2_normalize: true # whether to normalize the features so they have unit norm
sparse: true # whether to store sparse patches of features instead of the full feature maps
patch_size: 8 # the size of the feature patches if sparse
dtype: half # the data type of features when stored, half float or double
use_cache: false # whether to cache the features on file or keep them in memory
overwrite_cache: false # whether to overwrite the cache file if it already exists
cache_format: chunked
interpolation:
nodes: [[0.0, 0.0]] # grid over which to compute the cost, by default a single point
mode: BICUBIC # the interpolation algorithm
l2_normalize: true
ncc_normalize: false # only works if len(nodes)>1, mostly for photometric
mapping: # pixsfm.refine_colmap.PixSfM
dense_features: ${..dense_features}
KA: # keypoint adjustment
apply: true # whether to apply or instead skip
strategy: featuremetric # regular, or alternatively topological_reference (much faster)
interpolation: ${...interpolation} # we can use a different interpolation for KA
level_indices: null # we can optimize a subset of levels, by default all
split_in_subproblems: true # parallelize the optimization
max_kps_per_problem: 50 # parallelization, a lower value saves memory, conservative if -1
optimizer: # optimization problem and solving
loss:
name: cauchy # name of the loss function, among {huber, soft_l1, ...}
params: [0.25] # loss-specific parameters
solver:
function_tolerance: 0.0
gradient_tolerance: 0.0
parameter_tolerance: 1.0e-05
minimizer_progress_to_stdout: false # print a progress bar
max_num_iterations: 100 # maximum number of optimization iterations
max_linear_solver_iterations: 200
max_num_consecutive_invalid_steps: 10
max_consecutive_nonmonotonic_steps: 10
use_inner_iterations: false
use_nonmonotonic_steps: false
num_threads: 1
root_regularize_weight: -1 # prevent drift by adding edges to the root node, disabled if -1
print_summary: false # whether to print a detailed summary after completion
bound: 4.0 # constraint on the distance (in pixels) w.r.t. the initial values
num_threads: -1 # number of threads if parallelize in subproblems
BA: # bundle adjustment
apply: true # whether to apply or instead skip
strategy: feature_reference # regular, or alternatively {costmaps, patch_warp}
interpolation: ${...interpolation} # we can use a different interpolation for BA
level_indices: null # we can optimize a subset of levels, by default all
max_tracks_per_problem: 10 # parallelization of references/costmaps, a lower value saves memory
num_threads: -1
optimizer:
loss: # same config as KA.optimizer.loss
solver: # same config as KA.optimizer.solver
print_summary: false
refine_focal_length: true # whether to optimize the focal length
refine_principal_point: false # whether to optimize the principal points
refine_extra_params: true # whether to optimize distortion parameters
refine_extrinsics: true # whether to optimize the camera poses
references: # if strategy==feature_reference
loss: # what to minimize to compute the robust mean
name: cauchy
params: [0.25]
iters: 100 # number of iterations to compute the robust mean
num_threads: -1
repeats: 1
localization: # pixsfm.localization.main.QueryLocalizer
dense_features: ${..dense_features}
target_reference: nearest # how to select references, in {nearest, robust_mean, all_observations}
overwrite_features_sparse: null # overwrite dense_features.sparse in query localization only
references: # how to compute references
loss: # what to minimize to compute the robust mean, same as BA.references.loss
iters: 100
keep_observations: true # required for target_reference in {nearest, all_observations}
num_threads: -1
max_tracks_per_problem: 50 # parallelization of references, a lower value saves memory
unique_inliers: min_error # how we select unique matches for each 3D point
QKA: # query keypoint adjustment
apply: true # whether to apply or instead skip
interpolation: ${...interpolation}
level_indices: null
feature_inlier_thresh: -1 # discard points with high feature error, disabled if -1
stack_correspondences: False # Stack references for equal keypoints
optimizer:
loss: # same config as KA.optimizer.loss
name: trivial # L2, no robust loss function
params: []
solver: # same config as KA.optimizer.solver
print_summary: false
bound: 4.0 # constraint on the distance (in pixels) w.r.t. the initial values
PnP:
estimation: # pycolmap.absolute_pose_estimation
ransac:
max_error: 12 # inlier threshold in pixel reprojection error
estimate_focal_length: false # if the focal length is unknown
refinement: # refinement in pycolmap.absolute_pose_estimation
refine_focal_length: false
refine_extra_params: false
QBA: # query bundle adjuster
apply: true # whether to apply or instead skip
interpolation: ${...interpolation}
level_indices: null
optimizer:
loss: # same config as KA.optimizer.loss
solver: # same config as KA.optimizer.solver
print_summary: false
refine_focal_length: false
refine_principal_point: false
refine_extra_params: falseNote that the config supports variable interpolation through omegaconf.
Large-scale refinement
When dealing with large scenes or with a large number of images, memory is often a bottleneck. The configuration low_memory shows how to decrease the memory consumption by trading-off accuracy and speed.
[Click to expand]
The main improvements are:
dense_features- store as sparse patches:
sparse=true - reduce the size of the patches:
patch_size=8(or smaller) - store in a cache file:
use_cache=true
- store as sparse patches:
KA- chunk the optimization, loading only a subset of features at once:
split_in_subproblems=true - optimize at most around 50 keypoints per chunk:
max_kps_per_problem=50
- chunk the optimization, loading only a subset of features at once:
BA- use the costmap approximation:
strategy=costmaps(described in Section C of the paper)
- use the costmap approximation:
When runtime is a limitation, one can also reduce the runtime of KA by optimizing only costs with respect to the topological center of each track with KA.strategy=topological_reference.
Keypoints with large noise
[Click to expand]
Some keypoint detectors with low output resolution, like D2-Net, predict keypoints that are localized inaccurately. In this case, the refinement is highly beneficial but the default parameters are not optimal. It is necessary to increase the patch size and use multiple feature layers. An example configuration is given in pixsfm_eth3d_d2net to evaluate D2-Net on ETH3D.
Extending pixsfm
- To refine your own sparse keypoints or matcher, refer to Using your own local features or matcher in hloc.
- To add different dense features, see Using your own dense features.
- For a description of how dense features are accessed and stored, see doc/features.md.
- For a description of the internals of
pixsfm, see Design Principles.
Still having questions about pixsfm? Anything in the doc is unclear? Are you unsure whether it fits your use case? Please let us know by opening an issue!
Contributing
We welcome external contributions, especially to improve the following points:
- make
pixsfmwork on Windows - train and integrate dense features that are more compact with fewer dimensions
- build a conda package for pixsfm and pycolmap to not require installing COLMAP from source
- add examples on how to build featuremetric problems with pyceres
BibTex citation
Please consider citing our work if you use any code from this repo or ideas presented in the paper:
@inproceedings{lindenberger2021pixsfm,
author = {Philipp Lindenberger and
Paul-Edouard Sarlin and
Viktor Larsson and
Marc Pollefeys},
title = {{Pixel-Perfect Structure-from-Motion with Featuremetric Refinement}},
booktitle = {ICCV},
year = {2021},
}