OffscreenRenderer Segmentation fault on wsl2 and docker

[Six-Bit-TX]

Checklist

• I have searched for similar issues.
• For Python issues, I have tested with the latest development wheel.
• I have checked the release documentation and the latest documentation (for main branch).

Describe the issue

I've been trying to render Gaussian Marbles using open3d. Run into a segfault pretty quickly.
What I've tried so far:
1.

export DISPLAY=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):0
export LIBGL_ALWAYS_INDIRECT=0

result in eglInitialize failure.
2.
Starting from pytorch/pytorch:latest docker with wsl2 as backend and run into the same problem so I'm pretty sure it is a wsl specific issue.

Steps to reproduce the bug

import open3d.visualization.rendering as rendering
import faulthandler 
faulthandler.enable()
height = 1080
width = 1920
rendering.OffscreenRenderer(width=width, height=height)

Error message

[Open3D INFO] EGL headless mode enabled.
FEngine (64 bits) created at 0x7fed1ab43010 (threading is enabled)
eglInitialize failed
Fatal Python error: Segmentation fault

Current thread 0x00007fee064ca740 (most recent call first):
File "", line 1 in

Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, markupsafe._speedups, zmq.backend.cython._zmq, tornado.speedups, psutil._psutil_linux, psutil._psutil_posix, zstandard.backend_c, simplejson._speedups, charset_normalizer.md, sklearn.__check_build._check_build, lz4._version, lz4.frame._frame, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._ansari_swilk_statistics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, numexpr.interpreter, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, sklearn.neighbors._partition_nodes, sklearn.neighbors._ball_tree, sklearn.neighbors._kd_tree, sklearn.utils.arrayfuncs, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, sklearn.linear_model._sag_fast, sklearn.decomposition._online_lda_fast, sklearn.decomposition._cdnmf_fast, yaml._yaml, PIL._imaging (total: 190)
Segmentation fault (core dumped)

Expected behavior

No response

Open3D, Python and System information

- Operating system: Windows 11 64-bit running ubuntu22.04 using wsl2
- Python version: Python 3.10
- Open3D version: output from python: 0.18.0
- System architecture: x86
- Is this a remote workstation?: no
- How did you install Open3D?: pip
- Compiler version (if built from source): gcc 11.2.0

Additional information

No response

[rxba]

In case your numpy version is >=2.0.0 consider downgrading it to e.g. 1.26.4, that's the cause for a lot of the seemingly random segfaults on Open3D builds <=0.18 (#6874). If the issue persists, maybe someone else with WSL experience will chime in.

[Six-Bit-TX]

I'm actually using numpy 1.26.2

[rxba]

Does any of this WSL specific info help you? Otherwise I'm also out of ideas, hope someone else with more WSL experience can help.

[Six-Bit-TX]

Does any of this WSL specific info help you? Otherwise I'm also out of ideas, hope someone else with more WSL experience can help.

I've tried these settings and it didn't help. I was actually able to reproduce the error on ubuntu docker on a host ubuntu machine as well.

However native ubuntu works fine.