diff --git a/.gitignore b/.gitignore
index b2b9c4b..b40b8b0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -133,5 +133,7 @@ dmypy.json
 models/*
 data/*
 .idea
-src/nerf/cache/
-src/nerf/logs/
\ No newline at end of file
+**/cache/
+**/*logs/
+.venv/
+
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..d2577d9
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.7.7
diff --git a/data b/data
deleted file mode 120000
index dd97660..0000000
--- a/data
+++ /dev/null
@@ -1 +0,0 @@
-/home/nerfteam/data
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index c731793..7453503 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -44,13 +44,29 @@ version = "0.10.0"
 [package.dependencies]
 six = "*"
 
+[[package]]
+category = "main"
+description = "Decorators for Humans"
+name = "decorator"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
+version = "4.4.2"
+
+[[package]]
+category = "main"
+description = "Freetype python bindings"
+name = "freetype-py"
+optional = false
+python-versions = "*"
+version = "2.1.0.post1"
+
 [[package]]
 category = "main"
 description = "Google Authentication Library"
 name = "google-auth"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
-version = "1.14.1"
+version = "1.15.0"
 
 [package.dependencies]
 cachetools = ">=2.0.0,<5.0"
@@ -80,7 +96,7 @@ description = "HTTP/2-based RPC framework"
 name = "grpcio"
 optional = false
 python-versions = "*"
-version = "1.28.1"
+version = "1.29.0"
 
 [package.dependencies]
 six = ">=1.5.2"
@@ -93,6 +109,20 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 version = "2.9"
 
+[[package]]
+category = "main"
+description = "Image transformation, compression, and decompression codecs"
+name = "imagecodecs"
+optional = false
+python-versions = ">=3.6"
+version = "2020.2.18"
+
+[package.dependencies]
+numpy = ">=1.15"
+
+[package.extras]
+all = ["matplotlib (>=3.1)", "tifffile (>=2019.7.2)"]
+
 [[package]]
 category = "main"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
@@ -112,6 +142,22 @@ full = ["astropy", "gdal", "imageio-ffmpeg", "itk"]
 gdal = ["gdal"]
 itk = ["itk"]
 
+[[package]]
+category = "main"
+description = "Read metadata from Python packages"
+marker = "python_version < \"3.8\""
+name = "importlib-metadata"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+version = "1.6.0"
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["sphinx", "rst.linker"]
+testing = ["packaging", "importlib-resources"]
+
 [[package]]
 category = "main"
 description = "A fast implementation of the Cassowary constraint solver"
@@ -126,10 +172,12 @@ description = "Python implementation of Markdown."
 name = "markdown"
 optional = false
 python-versions = ">=3.5"
-version = "3.2.1"
+version = "3.2.2"
 
 [package.dependencies]
-setuptools = ">=36"
+[package.dependencies.importlib-metadata]
+python = "<3.8"
+version = "*"
 
 [package.extras]
 testing = ["coverage", "pyyaml"]
@@ -149,6 +197,30 @@ numpy = ">=1.11"
 pyparsing = ">=2.0.1,<2.0.4 || >2.0.4,<2.1.2 || >2.1.2,<2.1.6 || >2.1.6"
 python-dateutil = ">=2.1"
 
+[[package]]
+category = "main"
+description = "Python package for creating and manipulating graphs and networks"
+name = "networkx"
+optional = false
+python-versions = ">=3.5"
+version = "2.4"
+
+[package.dependencies]
+decorator = ">=4.3.0"
+
+[package.extras]
+all = ["numpy", "scipy", "pandas", "matplotlib", "pygraphviz", "pydot", "pyyaml", "gdal", "lxml", "pytest"]
+gdal = ["gdal"]
+lxml = ["lxml"]
+matplotlib = ["matplotlib"]
+numpy = ["numpy"]
+pandas = ["pandas"]
+pydot = ["pydot"]
+pygraphviz = ["pygraphviz"]
+pytest = ["pytest"]
+pyyaml = ["pyyaml"]
+scipy = ["scipy"]
+
 [[package]]
 category = "main"
 description = "NumPy is the fundamental package for array computing with Python."
@@ -195,7 +267,7 @@ description = "Protocol Buffers"
 name = "protobuf"
 optional = false
 python-versions = "*"
-version = "3.11.3"
+version = "3.12.0"
 
 [package.dependencies]
 setuptools = "*"
@@ -220,6 +292,37 @@ version = "0.2.8"
 [package.dependencies]
 pyasn1 = ">=0.4.6,<0.5.0"
 
+[[package]]
+category = "main"
+description = "python library for reading and writing collada documents"
+name = "pycollada"
+optional = false
+python-versions = "*"
+version = "0.7.1"
+
+[package.dependencies]
+python-dateutil = ">=2.2"
+
+[package.extras]
+prettyprint = ["lxml"]
+validation = ["lxml"]
+
+[[package]]
+category = "main"
+description = "Cross-platform windowing and multimedia library"
+name = "pyglet"
+optional = false
+python-versions = "*"
+version = "1.5.5"
+
+[[package]]
+category = "main"
+description = "Standard OpenGL bindings for Python"
+name = "pyopengl"
+optional = false
+python-versions = "*"
+version = "3.1.0"
+
 [[package]]
 category = "main"
 description = "Python parsing module"
@@ -228,6 +331,30 @@ optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 version = "2.4.7"
 
+[[package]]
+category = "main"
+description = "Easy-to-use Python renderer for 3D visualization"
+name = "pyrender"
+optional = false
+python-versions = "*"
+version = "0.1.40"
+
+[package.dependencies]
+Pillow = "*"
+PyOpenGL = "3.1.0"
+freetype-py = "*"
+imageio = "*"
+networkx = "*"
+numpy = "*"
+pyglet = ">=1.4.10"
+scipy = "*"
+six = "*"
+trimesh = "*"
+
+[package.extras]
+dev = ["flake8", "pre-commit", "pytest", "pytest-cov", "tox"]
+docs = ["sphinx", "sphinx-rtd-theme", "sphinx-automodapi"]
+
 [[package]]
 category = "main"
 description = "Extensions to the standard Python datetime module"
@@ -239,6 +366,17 @@ version = "2.8.1"
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+category = "main"
+description = "PyWavelets, wavelet transform module"
+name = "pywavelets"
+optional = false
+python-versions = ">=3.5"
+version = "1.1.1"
+
+[package.dependencies]
+numpy = ">=1.13.3"
+
 [[package]]
 category = "main"
 description = "YAML parser and emitter for Python"
@@ -291,6 +429,40 @@ version = "4.0"
 [package.dependencies]
 pyasn1 = ">=0.1.3"
 
+[[package]]
+category = "main"
+description = "Image processing in Python"
+name = "scikit-image"
+optional = false
+python-versions = ">=3.6"
+version = "0.17.2"
+
+[package.dependencies]
+PyWavelets = ">=1.1.1"
+imageio = ">=2.3.0"
+matplotlib = ">=2.0.0,<3.0.0 || >3.0.0"
+networkx = ">=2.0"
+numpy = ">=1.15.1"
+pillow = ">=4.3.0,<7.1.0 || >7.1.0,<7.1.1 || >7.1.1"
+scipy = ">=1.0.1"
+tifffile = ">=2019.7.26"
+
+[package.extras]
+docs = ["sphinx (>=1.8,<=2.4.4)", "numpydoc (>=0.9)", "sphinx-gallery (>=0.3.1)", "sphinx-copybutton", "pytest-runner", "scikit-learn", "matplotlib (>=3.0.1)", "dask (>=0.15.0)", "cloudpickle (>=0.2.1)", "pandas (>=0.23.0)", "seaborn (>=0.7.1)", "pooch (>=0.5.2)"]
+optional = ["simpleitk", "astropy (>=1.2.0)", "qtpy", "pyamg", "dask (>=0.15.0)", "cloudpickle (>=0.2.1)", "pooch (>=0.5.2)"]
+test = ["pytest (!=3.7.3)", "pytest-cov", "pytest-localserver", "flake8", "codecov"]
+
+[[package]]
+category = "main"
+description = "SciPy: Scientific Library for Python"
+name = "scipy"
+optional = false
+python-versions = ">=3.5"
+version = "1.4.1"
+
+[package.dependencies]
+numpy = ">=1.13.3"
+
 [[package]]
 category = "main"
 description = "Python 2 and 3 compatibility utilities"
@@ -335,27 +507,26 @@ version = "1.6.0.post3"
 
 [[package]]
 category = "main"
-description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-name = "torch"
+description = "Read and write TIFF(r) files"
+name = "tifffile"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "1.4.0"
+python-versions = ">=3.6"
+version = "2020.5.11"
 
-[[package]]
-category = "main"
-description = ""
-name = "torchsearchsorted"
-optional = false
-python-versions = "*"
-version = "1.1"
+[package.dependencies]
+imagecodecs = ">=2020.2.18"
+numpy = ">=1.15.1"
 
 [package.extras]
-test = ["pytest"]
+all = ["matplotlib (>=3.1)"]
 
-[package.source]
-reference = "1e0ffc3e0663ffda318b4e28348efd90313d08f3"
-type = "git"
-url = "https://github.com/aliutkus/torchsearchsorted"
+[[package]]
+category = "main"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+name = "torch"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "1.4.0"
 
 [[package]]
 category = "main"
@@ -385,6 +556,24 @@ version = "4.46.0"
 [package.extras]
 dev = ["py-make (>=0.1.0)", "twine", "argopt", "pydoc-markdown"]
 
+[[package]]
+category = "main"
+description = "Import, export, process, analyze and view triangular meshes."
+name = "trimesh"
+optional = false
+python-versions = "*"
+version = "3.6.39"
+
+[package.dependencies]
+numpy = "*"
+setuptools = "*"
+
+[package.extras]
+all = ["pillow", "requests", "python-fcl", "scikit-image", "jsonschema", "triangle", "pycollada", "pyglet", "sympy", "msgpack", "colorlog", "lxml", "meshio", "glooey", "scipy", "rtree", "svg.path", "psutil", "shapely", "setuptools", "networkx", "chardet", "xxhash"]
+docs = ["sphinx", "recommonmark", "pyopenssl", "sphinx-rtd-theme", "scipy", "jupyter", "networkx", "pypandoc", "numpy", "autodocsumm"]
+easy = ["pycollada", "xxhash", "sympy", "msgpack", "colorlog", "lxml", "pillow", "scipy", "requests", "rtree", "svg.path", "shapely", "setuptools", "jsonschema", "networkx", "chardet", "pyglet"]
+test = ["pytest-cov", "coveralls", "pyinstrument", "pytest"]
+
 [[package]]
 category = "main"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
@@ -422,8 +611,21 @@ version = "0.34.2"
 [package.extras]
 test = ["pytest (>=3.0.0)", "pytest-cov"]
 
+[[package]]
+category = "main"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+marker = "python_version < \"3.8\""
+name = "zipp"
+optional = false
+python-versions = ">=3.6"
+version = "3.1.0"
+
+[package.extras]
+docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
+testing = ["jaraco.itertools", "func-timeout"]
+
 [metadata]
-content-hash = "70bb88d6ce4509421ccfb8dfe0e2dd03ae460b0a72d5642594f8ec16ecfa319f"
+content-hash = "557540e621bea76ffa8489947cfc9538cb0c781007faedf02149c7d201dcc480"
 python-versions = "^3.7"
 
 [metadata.files]
@@ -446,55 +648,96 @@ cycler = [
     {file = "cycler-0.10.0-py2.py3-none-any.whl", hash = "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d"},
     {file = "cycler-0.10.0.tar.gz", hash = "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"},
 ]
+decorator = [
+    {file = "decorator-4.4.2-py2.py3-none-any.whl", hash = "sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760"},
+    {file = "decorator-4.4.2.tar.gz", hash = "sha256:e3a62f0520172440ca0dcc823749319382e377f37f140a0b99ef45fecb84bfe7"},
+]
+freetype-py = [
+    {file = "freetype-py-2.1.0.post1.tar.gz", hash = "sha256:76383bb3e59efa6ce0be1797ed70207d7d1e421611df3aceb269673c4a77c2cc"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-any.whl", hash = "sha256:0f14ffc1715e3d0f01e7c9d07980b10b7143595d9322bb6da3fc74db5251fa8c"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl", hash = "sha256:2fa866dd3d8beb73d03f0cf6b7cc51d0cd0ab9c8674c1d8bee2a1b299ee0731c"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-manylinux1_i686.whl", hash = "sha256:be7a97a964a23d41d7eed4fc834b243871845115728ef41390b9efede588e854"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:84e9879bb5f87dc1eb5342230a13e7ea10f520aa06a4d1c7b298208bbac08110"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-win32.whl", hash = "sha256:d179759b245bb01f5603acd071aea99d97d993a28c4e71fdc4bd7530c49931f9"},
+    {file = "freetype_py-2.1.0.post1-py2.py3-none-win_amd64.whl", hash = "sha256:eda77d57acdfe74d1cf3958b5fb5aaea21a2866f4e8d7632c06123d6014f9e0c"},
+]
 google-auth = [
-    {file = "google-auth-1.14.1.tar.gz", hash = "sha256:e63b2210e03c4ed829063b72c4af0c4b867c2788efb3210b6b9439b488bd3afd"},
-    {file = "google_auth-1.14.1-py2.py3-none-any.whl", hash = "sha256:0c41a453b9a8e77975bfa436b8daedac00aed1c545d84410daff8272fff40fbb"},
+    {file = "google-auth-1.15.0.tar.gz", hash = "sha256:fbf25fee328c0828ef293459d9c649ef84ee44c0b932bb999d19df0ead1b40cf"},
+    {file = "google_auth-1.15.0-py2.py3-none-any.whl", hash = "sha256:73b141d122942afe12e8bfdcb6900d5df35c27d39700f078363ba0b1298ad33b"},
 ]
 google-auth-oauthlib = [
     {file = "google-auth-oauthlib-0.4.1.tar.gz", hash = "sha256:88d2cd115e3391eb85e1243ac6902e76e77c5fe438b7276b297fbe68015458dd"},
     {file = "google_auth_oauthlib-0.4.1-py2.py3-none-any.whl", hash = "sha256:a92a0f6f41a0fb6138454fbc02674e64f89d82a244ea32f98471733c8ef0e0e1"},
 ]
 grpcio = [
-    {file = "grpcio-1.28.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:085bbf7fd0070b8d65e84aa32979f17cfe624d27b5ce23955ef770c19d2d9623"},
-    {file = "grpcio-1.28.1-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:f80d10bdf1a306f7063046321fd4efc7732a606acdd4e6259b8a37349079b704"},
-    {file = "grpcio-1.28.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:e9439d7b801c86df13c6cbb4c5a7e181c058f3c119d5e119a94a5f3090a8f060"},
-    {file = "grpcio-1.28.1-cp27-cp27m-win32.whl", hash = "sha256:271abbe28eb99fa5c70b3f272c0c66b67dab7bb11e1d29d8e616b4e0e099d29a"},
-    {file = "grpcio-1.28.1-cp27-cp27m-win_amd64.whl", hash = "sha256:df749ee982ec35ab76d37a1e637b10a92b4573e2b4e1f86a5fa8a1273c40a850"},
-    {file = "grpcio-1.28.1-cp27-cp27mu-linux_armv7l.whl", hash = "sha256:0ef6b380a588c2c6b29c6cfa0ba7f5d367beb33d5504bcc68658fa241ad498d2"},
-    {file = "grpcio-1.28.1-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:c6565cc92853af13237b2233f331efdad07339d27fe1f5f74256bfde7dc2f587"},
-    {file = "grpcio-1.28.1-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:97b5612fc5d4bbf0490a2d80bed5eab5b59112ef1640440c1a9ac824bafa6968"},
-    {file = "grpcio-1.28.1-cp35-cp35m-linux_armv7l.whl", hash = "sha256:4bef0756b9e0df78e8d67a5b1e0e89b7daf41525d575f74e1f14a993c55b680d"},
-    {file = "grpcio-1.28.1-cp35-cp35m-macosx_10_7_intel.whl", hash = "sha256:0c130204ff5de0b9f041bf3126db0d29369d69883592e4b0d3c19868ba0ced7e"},
-    {file = "grpcio-1.28.1-cp35-cp35m-manylinux2010_i686.whl", hash = "sha256:70ff2df0c1795c5cf585a72d95bb458838b40bad5653c314b9067ba819e918f9"},
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-    {file = "grpcio-1.28.1-cp35-cp35m-win32.whl", hash = "sha256:f83b0c91796eb42865451a20e82246011078ba067ea0744f7301e12a94ae2e1b"},
-    {file = "grpcio-1.28.1-cp35-cp35m-win_amd64.whl", hash = "sha256:c2e53eb253840f05278a8410628419ba7060815f86d48c9d83b6047de21c9956"},
-    {file = "grpcio-1.28.1-cp36-cp36m-linux_armv7l.whl", hash = "sha256:16f5523dacae5aaeda4cf900da7e980747f663298c38c18eb4e5317704aa007a"},
-    {file = "grpcio-1.28.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:3c7864d5ae63b787001b01b376f6315aef1a015aa9c809535235ed0ead907919"},
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-    {file = "grpcio-1.28.1-cp36-cp36m-win32.whl", hash = "sha256:5c2e81b6ab9768c43f2ca1c9a4c925823aad79ae95efb351007df4b92ebce592"},
-    {file = "grpcio-1.28.1-cp36-cp36m-win_amd64.whl", hash = "sha256:245564713cb4ac7bccb0f11be63781beb62299a44d8ab69031c859dbd9461728"},
-    {file = "grpcio-1.28.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f493ac4754717f25ace3614a51dd408a32b8bff3c9c0c85e9356e7e0a120a8c8"},
-    {file = "grpcio-1.28.1-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:3366bd6412c1e73acb1ee27d7f0c7d7dbee118ad8d98c957c8173691b2effeec"},
-    {file = "grpcio-1.28.1-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:4fe081862e58b8fbef0e479aefc9a64f8f17f53074df1085d8c1fe825a6e5df4"},
-    {file = "grpcio-1.28.1-cp37-cp37m-win32.whl", hash = "sha256:3893b39a0a17d857dc3a42fdb02a26aa53a59bfce49987187bcc0261647f1f55"},
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 torchvision = [
     {file = "torchvision-0.5.0-cp27-cp27m-macosx_10_7_x86_64.whl", hash = "sha256:35e9483858cf8a38debc647c74741605c5c12448d314aa96961082380aadf7e5"},
     {file = "torchvision-0.5.0-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:323500d349d8d91ce2662de41212e8eb1845c68dbf5d4f215ca1e94c7f20723b"},
@@ -738,6 +1062,10 @@ tqdm = [
     {file = "tqdm-4.46.0-py2.py3-none-any.whl", hash = "sha256:acdafb20f51637ca3954150d0405ff1a7edde0ff19e38fb99a80a66210d2a28f"},
     {file = "tqdm-4.46.0.tar.gz", hash = "sha256:4733c4a10d0f2a4d098d801464bdaf5240c7dadd2a7fde4ee93b0a0efd9fb25e"},
 ]
+trimesh = [
+    {file = "trimesh-3.6.39-py3-none-any.whl", hash = "sha256:3afb0848c97ce044e02f7e58ce7dc9ae7e75540fabfcc530cec3efdabdba9399"},
+    {file = "trimesh-3.6.39.tar.gz", hash = "sha256:13362c23e97050e64b36aa32a8c354234e42d93bc908d846602916ad9b7e6838"},
+]
 urllib3 = [
     {file = "urllib3-1.25.9-py2.py3-none-any.whl", hash = "sha256:88206b0eb87e6d677d424843ac5209e3fb9d0190d0ee169599165ec25e9d9115"},
     {file = "urllib3-1.25.9.tar.gz", hash = "sha256:3018294ebefce6572a474f0604c2021e33b3fd8006ecd11d62107a5d2a963527"},
@@ -750,3 +1078,7 @@ wheel = [
     {file = "wheel-0.34.2-py2.py3-none-any.whl", hash = "sha256:df277cb51e61359aba502208d680f90c0493adec6f0e848af94948778aed386e"},
     {file = "wheel-0.34.2.tar.gz", hash = "sha256:8788e9155fe14f54164c1b9eb0a319d98ef02c160725587ad60f14ddc57b6f96"},
 ]
+zipp = [
+    {file = "zipp-3.1.0-py3-none-any.whl", hash = "sha256:aa36550ff0c0b7ef7fa639055d797116ee891440eac1a56f378e2d3179e0320b"},
+    {file = "zipp-3.1.0.tar.gz", hash = "sha256:c599e4d75c98f6798c509911d08a22e6c021d074469042177c8c86fb92eefd96"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 9dbae1e..b091849 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,8 +1,8 @@
 [tool.poetry]
-name = "nerfmeshes"
+name = "cchiv-nerf"
 version = "0.1.0"
 description = "A mesh reconstruction tool build around Neural Radiance Fields"
-authors = ["Benedikt Wiberg <benedikt.wiberg@gmail.com>"]
+authors = ["Cristian Chivrga <cchivriga@gmail.com>"]
 license = "MIT"
 
 [tool.poetry.dependencies]
@@ -15,8 +15,11 @@ torchvision = "^0.5.0"
 tqdm = "^4.46.0"
 opencv-python-headless = "^4.2.0"
 matplotlib = "^3.2.1"
-torchsearchsorted = { git = "https://github.com/aliutkus/torchsearchsorted"}
-torch = "1.4.0"
+trimesh = "^3.6.38"
+pyrender = "^0.1.40"
+pycollada = "^0.7.1"
+scipy = "^1.4.1"
+scikit-image = "^0.17.2"
 
 [tool.poetry.dev-dependencies]
 
diff --git a/src/nerf/cache_dataset.py b/src/cache_dataset.py
similarity index 100%
rename from src/nerf/cache_dataset.py
rename to src/cache_dataset.py
diff --git a/src/nerf/config/default.yml b/src/config/default.yml
similarity index 100%
rename from src/nerf/config/default.yml
rename to src/config/default.yml
diff --git a/src/nerf/config/fern.yml b/src/config/fern.yml
similarity index 100%
rename from src/nerf/config/fern.yml
rename to src/config/fern.yml
diff --git a/src/nerf/config/lego.yml b/src/config/lego.yml
similarity index 100%
rename from src/nerf/config/lego.yml
rename to src/config/lego.yml
diff --git a/src/nerf/config/llff.yml b/src/config/llff.yml
similarity index 100%
rename from src/nerf/config/llff.yml
rename to src/config/llff.yml
diff --git a/src/nerf/config/tiny.yaml b/src/config/tiny.yaml
similarity index 100%
rename from src/nerf/config/tiny.yaml
rename to src/config/tiny.yaml
diff --git a/src/nerf/eval_nerf.py b/src/eval_nerf.py
similarity index 100%
rename from src/nerf/eval_nerf.py
rename to src/eval_nerf.py
diff --git a/src/nerf/lieutils.py b/src/lieutils.py
similarity index 100%
rename from src/nerf/lieutils.py
rename to src/lieutils.py
diff --git a/src/mesh_nerf.py b/src/mesh_nerf.py
new file mode 100644
index 0000000..2dfff06
--- /dev/null
+++ b/src/mesh_nerf.py
@@ -0,0 +1,307 @@
+import argparse
+import os
+import time
+import numpy as np
+import torch
+import torchvision
+import yaml
+
+from pathlib import Path
+from nerf import run_network
+from skimage import measure
+from scipy.spatial import KDTree
+from tqdm import tqdm
+
+from nerf import get_minibatches
+
+from nerf import (
+    CfgNode,
+    load_blender_data,
+    load_llff_data,
+    models,
+    get_embedding_function,
+    predict_and_render_radiance,
+    volume_render_radiance_field
+)
+
+def export_obj(vertices, triangles, diffuse, normals, filename):
+    """
+    Exports a mesh in the (.obj) format.
+    """
+
+    with open(filename, 'w') as fh:
+
+        for index, v in enumerate(vertices):
+            fh.write("v {} {} {} {} {} {}\n".format(*v, *diffuse[index]))
+
+        for n in normals:
+            fh.write("vn {} {} {}\n".format(*n))
+
+        for f in triangles:
+            fh.write("f")
+            for index in f:
+                fh.write(" {}//{}".format(index + 1, index + 1))
+
+            fh.write("\n")
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--config", type = str, required = True, help = "Path to (.yml) config file."
+    )
+    parser.add_argument(
+        "--base-dir",
+        type = str,
+        required = False,
+        help = "Override the default base dir.",
+    )
+    parser.add_argument(
+        "--checkpoint",
+        type = str,
+        required = True,
+        help = "Checkpoint / pre-trained model to evaluate.",
+    )
+    parser.add_argument(
+        "--save-dir", type = str, help = "Save mesh to this directory, if specified."
+    )
+
+    parser.add_argument('--cache-mesh', dest = 'cache_mesh', action = 'store_true')
+    parser.add_argument('--no-cache-mesh', dest = 'cache_mesh', action = 'store_false')
+    parser.set_defaults(cache_mesh = True)
+
+    configargs = parser.parse_args()
+
+    # Read config file.
+    cfg, model_name = None, None
+    with open(configargs.config, "r") as f:
+        cfg_dict = yaml.load(f, Loader = yaml.FullLoader)
+        cfg, model_name = CfgNode(cfg_dict), Path(f.name).stem
+
+    # Device on which to run.
+    device = "cpu" if not torch.cuda.is_available() else "cuda"
+
+    encode_position_fn = get_embedding_function(
+        num_encoding_functions = cfg.models.coarse.num_encoding_fn_xyz,
+        include_input = cfg.models.coarse.include_input_xyz,
+        log_sampling = cfg.models.coarse.log_sampling_xyz,
+    )
+
+    encode_direction_fn = None
+    if cfg.models.coarse.use_viewdirs:
+        encode_direction_fn = get_embedding_function(
+            num_encoding_functions = cfg.models.coarse.num_encoding_fn_dir,
+            include_input = cfg.models.coarse.include_input_dir,
+            log_sampling = cfg.models.coarse.log_sampling_dir,
+        )
+
+    # Initialize a coarse resolution model.
+    model_coarse = getattr(models, cfg.models.coarse.type)(
+        num_encoding_fn_xyz = cfg.models.coarse.num_encoding_fn_xyz,
+        num_encoding_fn_dir = cfg.models.coarse.num_encoding_fn_dir,
+        include_input_xyz = cfg.models.coarse.include_input_xyz,
+        include_input_dir = cfg.models.coarse.include_input_dir,
+        use_viewdirs = cfg.models.coarse.use_viewdirs,
+    ).to(device)
+
+    # If a fine-resolution model is specified, initialize it.
+    model_fine = None
+    if hasattr(cfg.models, "fine"):
+        model_fine = getattr(models, cfg.models.fine.type)(
+            num_encoding_fn_xyz = cfg.models.fine.num_encoding_fn_xyz,
+            num_encoding_fn_dir = cfg.models.fine.num_encoding_fn_dir,
+            include_input_xyz = cfg.models.fine.include_input_xyz,
+            include_input_dir = cfg.models.fine.include_input_dir,
+            use_viewdirs = cfg.models.fine.use_viewdirs,
+        ).to(device)
+
+    checkpoint = torch.load(configargs.checkpoint)
+    model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
+    if checkpoint["model_fine_state_dict"]:
+        try:
+            model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
+        except:
+            print(
+                "The checkpoint has a fine-level model, but it could "
+                "not be loaded (possibly due to a mismatched config file."
+            )
+
+    model_coarse.eval()
+    if model_fine:
+        model_fine.eval()
+
+    # Mesh Extraction
+    N = 128
+    iso_value = 32
+    batch_size = 1024
+    density_samples_count = 6
+    chunk = int(density_samples_count / 2)
+    distance_length = 0.001
+    distance_threshold = 0.001
+    view_disparity = 0.2
+    limit = 1.2
+    t = np.linspace(-limit, limit, N)
+    sampling_method = 0
+    adjust_normals = False
+    specific_view = False
+    plane_near = 0
+    plane_far = 6
+
+    vertices, triangles, normals, diffuse = None, None, None, None
+    if configargs.cache_mesh:
+        query_pts = np.stack(np.meshgrid(t, t, t), -1).astype(np.float32)
+        pts = torch.from_numpy(query_pts)
+        dimension = pts.shape[-1]
+
+        pts_flat = pts.reshape((-1, dimension))
+        pts_flat_batch = pts_flat.reshape((-1, batch_size, dimension))
+
+        density = np.zeros((pts_flat.shape[0]))
+        for idx, batch in enumerate(tqdm(pts_flat_batch)):
+            batch = batch.cuda()
+
+            embedded = encode_position_fn(batch)
+            if encode_direction_fn is not None:
+                embedded_dirs = encode_direction_fn(batch)
+                embedded = torch.cat((embedded, embedded_dirs), dim = -1)
+
+            result_batch = model_fine(embedded)
+
+            # Extracting the density
+            density[idx * batch_size: (idx + 1) * batch_size] = result_batch[..., 3].cpu().detach().numpy()
+
+        # Create a 3D density grid
+        grid_alpha = density.reshape((N, N, N))
+
+        # Extracting iso-surface triangulated
+        vertices, triangles, normals, values = measure.marching_cubes(grid_alpha, iso_value)
+        vertices = np.ascontiguousarray(vertices)
+        normals = np.ascontiguousarray(normals)
+
+        # Query directly without specific-views
+        if specific_view:
+            targets = torch.from_numpy(vertices) / N * 2 * limit - limit
+            targets = targets[:, [1, 0, 2]]
+            stride = 512
+            diffuse = np.zeros((len(targets), 3))
+            for idx in range(0, len(targets) // stride + 1):
+                offset1 = stride * idx
+                offset2 = np.minimum(stride * (idx + 1), len(vertices))
+                batch = targets[offset1:offset2].to(device)
+
+                embedded = encode_position_fn(batch)
+                if encode_direction_fn is not None:
+                    embedded_dirs = encode_direction_fn(batch)
+                    embedded = torch.cat((embedded, embedded_dirs), dim = -1)
+
+                result_batch = model_fine(embedded)
+
+                # Query the whole diffuse map
+                diffuse[offset1:offset2] = result_batch[..., :3].cpu().detach().numpy()
+
+        if adjust_normals:
+            # Re-adjust normals based on NERF's density grid
+            # Create a density KDTree look-up table
+            tree = KDTree(pts_flat) if sampling_method == 0 else None
+
+            # Create some density samples
+            density_samples = np.linspace(-distance_length, distance_length, density_samples_count)[:, np.newaxis]
+
+            # Adjust normals with the assumption of having proper geometry
+            print("Adjusting normals")
+            for index, vertex in enumerate(tqdm(vertices)):
+                vertex_norm = vertex[[1, 0, 2]] / N * 2 * limit - limit
+                vertex_direction = normals[index][[1, 0, 2]]
+
+                # Sample points across the ray direction (a.k.a normal)
+                samples = vertex_norm[np.newaxis, :].repeat(density_samples_count, 0) + \
+                    vertex_direction[np.newaxis, :].repeat(density_samples_count, 0) * density_samples
+
+                def extract_cum_density(samples):
+                    inliers_indices = None
+                    if sampling_method == 0:
+                        # Sample 1th nearest neighbor
+                        distances, indices = tree.query(samples, 1)
+
+                        # Filter outliers
+                        inliers_indices = indices[distances <= distance_threshold]
+                    elif sampling_method == 1:
+                        # Sample based on grid proximity
+                        indices = (np.around((samples + limit) / 2 / limit * N) * N ** np.arange(2, -1, -1)).sum(1).astype(int)
+
+                        # Filtering exceeding boundaries
+                        inliers_indices = indices[~(indices >= N ** 3)]
+                    else:
+                        # Sample based on re-computing the radiance field
+                        indices = (np.around((samples + limit) / 2 / limit * N) * N ** np.arange(2, -1, -1)).sum(1).astype(int)
+
+                        # Filtering exceeding boundaries
+                        inliers_indices = indices[~(indices >= N ** 3)]
+
+                    return density[inliers_indices].sum()
+
+                # Extract densities
+                sample_density_1 = extract_cum_density(samples[:chunk])
+                sample_density_2 = extract_cum_density(samples[chunk:])
+
+                # Re-direct the normal
+                if sample_density_1 < sample_density_2:
+                    normals[index] *= (-1)
+
+        np.save(os.path.join(configargs.save_dir, "mesh_cache.npy"), (vertices, triangles, normals))
+        print("Saved successfully")
+    else:
+        vertices, triangles, normals = np.load(os.path.join(configargs.save_dir, "mesh_cache.npy"), allow_pickle = True)
+
+    # Extracting the diffuse color
+    # Ray targets
+    targets = torch.from_numpy(vertices) / N * 2 * limit - limit
+    # Swap x-axis and y-axis
+    targets = targets[:, [1, 0, 2]]
+
+    # Ray directions
+    directions = torch.from_numpy(normals)
+
+    # Ray directions swapped based on Marching Cubes algorithm
+    directions = directions[:, [1, 0, 2]]
+
+    # Ray origins
+    # ray_origins = length * directions
+    ray_origins = targets + view_disparity * directions
+
+    # Ray directions
+    ray_directions_loose = targets  - ray_origins
+    ray_directions = ray_directions_loose / ray_directions_loose.norm(dim = 1).unsqueeze(1)
+
+    near = plane_near * torch.ones_like(ray_directions[..., :1])
+    far = plane_far * torch.ones_like(ray_directions[..., :1])
+
+    # Generating ray batches
+    rays = torch.cat((ray_origins, ray_directions, near, far), dim = -1)
+    if cfg.nerf.use_viewdirs:
+        # Provide ray directions as input
+        view_dirs = ray_directions / ray_directions.norm(p = 2, dim = -1).unsqueeze(-1)
+        rays = torch.cat((rays, view_dirs.view((-1, 3))), dim = -1)
+
+    ray_batches = get_minibatches(rays, chunksize = 2048)
+
+    pred = []
+    for ray_batch in ray_batches:
+        # move to appropriate device
+        ray_batch = ray_batch.to(device)
+
+        _, _, _, diffuse, _, _ = predict_and_render_radiance(ray_batch, model_coarse, model_fine, cfg,
+                mode = "validation",
+                encode_position_fn = encode_position_fn,
+                encode_direction_fn = encode_direction_fn,
+        )
+
+        pred.append(diffuse.cpu().detach())
+
+    # Query the whole diffuse map
+    diffuse = torch.cat(pred, dim = 0).numpy()
+
+    # Export model
+    export_obj(vertices, triangles, diffuse, normals, os.path.join(configargs.save_dir, f"{model_name}.obj"))
+
+if __name__ == "__main__":
+    main()
diff --git a/src/nerf/nerf/__init__.py b/src/nerf/__init__.py
similarity index 100%
rename from src/nerf/nerf/__init__.py
rename to src/nerf/__init__.py
diff --git a/src/nerf/nerf/cfgnode.py b/src/nerf/cfgnode.py
similarity index 100%
rename from src/nerf/nerf/cfgnode.py
rename to src/nerf/cfgnode.py
diff --git a/src/nerf/nerf/load_blender.py b/src/nerf/load_blender.py
similarity index 100%
rename from src/nerf/nerf/load_blender.py
rename to src/nerf/load_blender.py
diff --git a/src/nerf/nerf/load_llff.py b/src/nerf/load_llff.py
similarity index 100%
rename from src/nerf/nerf/load_llff.py
rename to src/nerf/load_llff.py
diff --git a/src/nerf/nerf/metrics.py b/src/nerf/metrics.py
similarity index 100%
rename from src/nerf/nerf/metrics.py
rename to src/nerf/metrics.py
diff --git a/src/nerf/nerf/models.py b/src/nerf/models.py
similarity index 100%
rename from src/nerf/nerf/models.py
rename to src/nerf/models.py
diff --git a/src/nerf/nerf/nerf_helpers.py b/src/nerf/nerf_helpers.py
similarity index 100%
rename from src/nerf/nerf/nerf_helpers.py
rename to src/nerf/nerf_helpers.py
diff --git a/src/nerf/nerf/train_utils.py b/src/nerf/train_utils.py
similarity index 100%
rename from src/nerf/nerf/train_utils.py
rename to src/nerf/train_utils.py
diff --git a/src/nerf/nerf/volume_rendering_utils.py b/src/nerf/volume_rendering_utils.py
similarity index 100%
rename from src/nerf/nerf/volume_rendering_utils.py
rename to src/nerf/volume_rendering_utils.py
diff --git a/src/nerf/tiny_nerf.py b/src/tiny_nerf.py
similarity index 100%
rename from src/nerf/tiny_nerf.py
rename to src/tiny_nerf.py
diff --git a/src/nerf/train_nerf.py b/src/train_nerf.py
similarity index 100%
rename from src/nerf/train_nerf.py
rename to src/train_nerf.py