Visual-SLAM Developer Roadmap - 2026
Visual-SLAM is a special case of 'Simultaneous Localization and Mapping ' in which a camera is used to gather exteroceptive sensory data.
Below is a set of topics you need to understand for Visual-SLAM, ranging from absolute-beginner level to getting ready to work as a Visual-SLAM engineer/researcher.
Visual-SLAM is often portrayed as a rather difficult topic - many think good C++ programming skills and a deep understanding of mathematics are necessary.
On the other hand, there are not many courses provided for beginners, especially in non-English languages.
I made this roadmap to share my thoughts and experience on studying Visual-SLAM, and hopefully beginners can get a grasp of where to start.
Purpose of these Roadmaps
The purpose of these roadmaps is to give you an idea about the general overview of Visual-SLAM, and to guide you if you are confused about where to start.
Acknowledge that SLAM has a relatively high entry barrier - it's not because it requires understanding difficult mathematics, but because it requires equipping yourself with various types of skills. Don't feel overwhelmed - you don't need to learn everything if you are just getting started. Instead, enjoy the journey itself and progress topic by topic. The result will be very rewarding.
Level
Topic
Focus
1 Beginner Math, programming, and camera/image fundamentals
2 Getting Familiar Geometry, optimization, and the anatomy of a SLAM system
3 Monocular SLAM Classical monocular SLAM — feature-based, direct, semi-direct, SfM, dynamic scenes
4 RGB-D SLAM Dense tracking and volumetric/surfel fusion with depth sensors
5 Deep Learning + SLAM Learned frontends, differentiable backends, end-to-end systems, foundation-model & neural SLAM, scene understanding
6 VIO / VINS Fusing cameras with IMUs — filtering vs optimization
7 Stereo SLAM Metric scale and depth from stereo pairs
8 Collaborative SLAM Multi-robot mapping, inter-robot loop closure, map merging
9 LiDAR & Visual-LiDAR LiDAR odometry and tight camera–LiDAR–IMU fusion
10 Event Camera SLAM Asynchronous vision for HDR and high-speed motion
11 World Models & Spatial AI From SLAM maps to learned world representations
Level 2: Getting Familiar with SLAM
C++ C Git/GitHub OpenCV Python Bash/Linux Math libraries C++/Python interop ROS/ROS2 Docker
Programming (optional — engineering skills for SLAM jobs)
Concurrency Edge deployment Mobile C# CI/CD Simulation
Certifiably Optimal Algorithms
System
Author/Year
Key Concepts
SE-Sync Rosen 2019 Certifiable pose graph optimization via SDP + Riemannian opt (arXiv 2016, IJRR 2019)
TEASER++ Yang 2020 Point cloud registration, 90%+ outlier robust, TLS + Max Clique (T-RO/RSS 2020)
GNC Yang 2020 Graduated Non-Convexity, continuation from convex → robust cost
QUASAR Yang 2019 Certifiably optimal rotation search (Wahba problem with outliers), quaternion QCQP + SDP relaxation
Least Squares Optimization Factor Graph Optimization Gaussian Belief Propagation
Camera device Stereo vision , RGB-D , Structured light , Active IR/ToF LiDAR Visual-LiDAR fusion IMU RADAR Sensor fusion, Extended Kalman filter Sonar Multi-sensor calibration Kalibr )
Level 3: Monocular Visual-SLAM
VO vs SLAM Scale ambiguity Covisibility graph Visual Place Recognition (VPR) Self-supervised depth
System
Author/Year
Key Concepts
Visual Odometry Nistér 2004 Five-point essential matrix solver, RANSAC, Triangulation, VO (local-only, no loop closure)
MonoSLAM Davison 2007 First real-time monocular SLAM , EKF-based, single camera, sparse 3D map, probabilistic feature initialization
PTAM Klein & Murray 2007 FAST feature, Tracking, Frontend/Backend separation , Parallel threads, Keyframe, Mapping, Bundle adjustment, Manual initialization
Visual-SLAM why filter? Strasdat 2012 Bundle adjustment, Scale-aware BA, Motion-only BA
ORB-SLAM Mur-Artal 2015 ORB keypoint, Automatic initialization (Homography vs Fundamental selection) , Tracking thread, Local (covisibility-based) BA + global BA on loop closure, Local mapping, Large-scale, Loop closure, Bag of Visual Words, Global optimization, Covisibility graph, Map point management (culling, merging)
Pop-up SLAM Yang 2016 Line/Plane features
PL-SLAM Pumarola 2017 Point/Line features
ORB-SLAM2 Mur-Artal 2017 → Stereo SLAM, → RGB-D SLAM
CubeSLAM Yang 2019 Monocular 3D cuboid detection + SLAM, 9-DoF object representation
OpenVSLAM Sumikura 2019 ORB-based SLAM framework, perspective/fisheye/equirectangular camera models, map save/load + localization mode
Stella-VSLAM Community 2021 OpenVSLAM successor, license reboot (→ also in Level 7)
UcoSLAM Muñoz-Salinas 2019 Fiducial markers
DeepFusion Laidlow 2019 Dense monocular reconstruction, semi-dense MVS + CNN depth/gradient predictions, probabilistic fusion with learned uncertainties
ORB-SLAM3 Campos 2020 Monocular + Stereo + VIO, Multi-map, IMU integration
DXSLAM Li 2020 Deep features for SLAM
PyCuVSLAM NVIDIA 2025 Python + CUDA GPU-accelerated VSLAM toolkit (cuVSLAM wrapper; stereo/multi-camera VIO)
System
Author/Year
Key Concepts
DTAM Newcombe 2011 Dense mapping, Keyframe mapping, GPGPU
LSD-SLAM Engel 2014 Photometric error minimization, High gradient pixels/edges, Large scale, Loop closure, Pose graph optimization
DSO Engel 2016 Photometric bundle adjustment, Sliding window BA, No loop closure/global optimization
LDSO Gao 2018 DSO + Loop closure (BoW-based), addresses DSO's main weakness
CNN-SLAM Tateno 2017 Depth from LSD-SLAM + deep depth, Semantic label
DVSO Yang 2018 Deep single image depth estimation, StackNet
D3VO Yang 2020 Deep single image depth estimation, Deep pose, Deep aleatoric uncertainty
System
Author/Year
Key Concepts
COLMAP Schönberger 2016 De-facto standard incremental SfM + MVS pipeline (C++/CUDA, pycolmap bindings)
GLOMAP Pan 2024 Global SfM revisited — COLMAP-compatible, much faster mapping
InstantSfM Zhong 2025 GPU-native sparse-aware SfM pipeline, large speedups over COLMAP
Level 4: RGB-D Visual-SLAM
Depth from sensor Frame-to-model tracking ICP ) instead of frame-to-frameTSDF vs Surfel maps
Intel RealSense D series
Orbbec Femto series (Azure Kinect successor), Orbbec Astra
Luxonis OAK-D
Legacy (discontinued): Microsoft Kinect v1/v2, Azure Kinect DK, Occipital Structure Core
System
Author/Year
Key Concepts
ICP Besl & McKay 1992 Iterative Closest Point, closest-point correspondence, closed-form rigid transform, local convergence (needs initialization), foundation of 3D-3D registration
DTAM Newcombe 2011
→ see Level 3 Direct SLAM
KinectFusion Newcombe 2011 GPGPU, Tracking (project depth → 3D, surface normal, coarse-to-fine ICP), Mapping (volumetric integration, TSDF), Robust to small scene changes, Cannot model deformation, Map growth cubic, Room-size only
Double Window Optimisation Strasdat 2011 Inner window (local BA) + outer window (pose graph), covisibility graph, constant-time optimization
Kintinuous Whelan 2012 Volume shift, Geometric, Photometric, dBoW+SURF, Optimization, Loop closure
RGBD-SLAM-V2 Endres 2013 Tracking (color image, visual features, depth image, point cloud, transformation), Mapping (OctoMap 2013)
SLAM++ Salas-Moreno 2013 Object-oriented SLAM
DVO Kerl 2013 Keyframe, Depth, Direct method, Optimization, Loop closure
RTAB-Map Labbé 2014 Loop closure, Map merge, Multi-session memory management
MRS-Map Stückler 2014 Multi-resolution surfel maps in an octree, shape + color statistics per surfel, noise-aware RGB-D registration, real-time on CPU
ElasticFusion Whelan 2015 Active: frame-to-model tracking (photometric + geometric), joint optimization, fused surfel-based model reconstruction · Inactive: local loop closure (model-to-model local surface, submodel separation), global loop closure (randomised fern encoding, non-rigid space deformation)
DynamicFusion Newcombe 2015 6D motion field, Deformable scene
ORB-SLAM2 Mur-Artal 2017
Bundle adjustment, Sparse reconstruction (→ also in Level 3)
BundleFusion Dai 2016 Local-to-global optimization, Sparse RGB feature, Coarse global pose estimation, Fine pose refinement (geometric + photometric)
SemanticFusion McCormac 2016 Deep Learning CNN, Deep Semantic SLAM
InfiniTAM v3 Prisacariu 2017 Tracking (scene raycast, depth image, RGB image), Relocalization (random ferns), Mapping (TSDF reconstruction, voxel hashing, surfel reconstruction)
Fusion++ McCormac & Clark 2018 Deep Learning CNN, Mask-RCNN instance segmentation, Object-level SLAM, No prior, Object-level TSDF reconstruction
PointFusion / DenseFusion Xu 2018 / Wang 2019 RGB-D object 6-DoF pose estimation, point cloud + image feature fusion (object frontend for object-level SLAM)
BAD SLAM Schöps 2019 Direct RGB-D bundle adjustment, surfel map, real-time GPU BA, ETH3D benchmark
RTAB-Map Labbé 2019 Multi-sensor RGB-D/LiDAR support, light-source detection (2016)
MoreFusion Wada 2020 DL instance segmentation, Object-level volumetric fusion, Volumetric pose prediction, 3D scene reconstruction, Collision-based refinement, Semantic SLAM, Object pose estimation, CAD object fitting
NodeSLAM Sucar 2020
Occupancy VAE, Object-level SLAM (→ also in Level 5 Latent Representation)
DSP-SLAM Wang (UCL) 2021 DeepSDF shape prior + ORB-SLAM2, object-level dense reconstruction (mono/stereo/LiDAR)
Level 5: Applying Deep Learning
Learned vs hand-crafted Differentiability Foundation models
Level 5 is organized into five pillars:
A. Frontend — learned perception components replacing hand-crafted modules
B. Backend — learned/certifiable optimization replacing classical solvers
C. Systems — end-to-end deep VO/SLAM pipelines
D. Scene Understanding — semantic, language, and relational reasoning on SLAM maps
E. Foundation-Model & Neural SLAM — pointmap transformers, NeRF- and 3DGS-based dense SLAM systems
A. Deep Frontend — Perception Feature Detection & Matching
System
Author/Year
Key Concepts
NetVLAD Arandjelović 2016 VLAD, place recognition
SuperPoint DeTone 2017 Homographic Adaptation, Self-supervised, VGG encoder + detector/descriptor heads
HardNet Mishchuk 2017 Learned local descriptor
R2D2 Revaud 2019 Repeatable + Reliable detector/descriptor, explicit repeatability/reliability maps
KeyNet Barroso-Laguna 2019 Learned keypoint detector
HF-Net Sarlin 2019 Global feature, Local feature, Visual localization
SuperGlue Sarlin 2020 Self/Cross-attention GNN, Sinkhorn optimal assignment, dustbin for outliers
DISK Tyszkiewicz 2020 Policy gradient (RL) training, match success/failure as reward
Patch NetVLAD Hausler 2021 Multi-scale patch-level VLAD
LoFTR Sun 2021 Detector-free, Transformer coarse-to-fine dense matching
LightGlue Lindenberger 2023 Adaptive depth/width, 5-10× faster than SuperGlue
XFeat Potje 2024 0.3M params, 1400 FPS (RTX 4090), 64-dim descriptor, embedded-friendly
RoMa Edstedt 2024 DINOv2 foundation feature + coarse-to-fine dense matching
DeDoDe Edstedt 2024 Joint detect-and-describe in one stage
RoMa v2 Edstedt 2025 Harder-better-faster-denser dense feature matching
System
Author/Year
Key Concepts
MonoDepth Godard 2016 Left-Right photometric consistency, self-supervised
MiDaS Ranftl 2020 Multi-dataset mixing, scale-and-shift invariant loss, relative depth
DPT Ranftl 2021 Dense Prediction Transformer (ViT backbone), global context
ZoeDepth Bhat 2023 Zero-shot metric depth, Metric Bins Module
Metric3D Yin 2023 Camera intrinsic-conditioned metric depth, Canonical Camera Space
Depth Anything Yang 2024 62M images, foundation model for monocular depth
Depth Anything V2 Yang 2024 Improved with synthetic data, better edge preservation
Depth Anything 3 Lin 2025 Any-view geometry from arbitrary inputs, depth-ray prediction target, single plain transformer (DINOv2), teacher-student training
Marigold Ke 2024 Stable Diffusion for depth, fine detail, uncertainty via sampling
Align3R Lu 2025 Video temporal consistency, DUSt3R-based, CVPR 2025 Highlight
Masked Depth Modeling (LingBot-Depth) Tan 2026 Fixes RGB-D failures on glass/mirrors/metal
Optical Flow & Scene Flow Camera Pose Regression & Relocalization Object Detection & Segmentation for SLAM
System
Author/Year
Key Concepts
YOLO Redmon 2016→2024 Real-time object detection, Ultralytics ecosystem
DETR Carion 2020 Transformer detection, anchor-free, no NMS
RT-DETR Zhao (Baidu) 2023 Real-time DETR, YOLO-speed + Transformer quality
RF-DETR Robinson 2025 Weight-sharing NAS over DETRs, accuracy-latency Pareto tuning, first real-time detector past 60 AP on COCO
SAM Kirillov 2023 Segment Anything, prompt-based, Foundation Model
SAM 2 Meta 2024 Video segmentation, Memory Attention, temporal consistency
SAM 3 Carion 2025 Promptable concept segmentation (noun-phrase / exemplar prompts), presence head, detector + memory-based video tracker
Grounding DINO Liu 2023 Text-prompted detection → SAM pipeline (Grounded SAM)
Open-YOLO 3D Boudjoghra 2024 2D open-vocab detection → 3D instance seg, 16× faster
B. Deep Backend — Optimization Differentiable Bundle Adjustment C. End-to-End Deep VO / SLAM Systems Self-supervised & Learned VO
System
Author/Year
Key Concepts
DeepVO Wang 2017 Supervised learning
SfM-Learner Zhou 2017 Unsupervised, deep depth + deep pose
DeMoN Ummenhofer 2017 Depth + Motion from two frames, encoder-decoder
UndeepVO Li 2018 Stereo self-supervised, absolute scale recovery
DeepTAM Zhou 2018 Deep tracking and mapping, cost volume based
DeepV2D Teed 2018 Iterative depth from video, differentiable geometry layers
Depth from Videos in the Wild Gordon 2019 Unconstrained video depth, learned camera intrinsics
Neural Ray Surfaces Vasiljevic 2020 Learned ray surface model, non-pinhole cameras
GradSLAM Murthy 2020 Differentiable SLAM framework (PyTorch, supports multiple SLAM backends)
DeepSLAM Li 2020 TrackingNet, MappingNet, LoopNet
MonoRec Wimbauer 2021 Self-supervised monocular 3D reconstruction, moving objects
TANDEM Koestler 2021 Real-time tracking + dense mapping via MVS depth, DSO-based
Learning-based SLAM Systems Latent Representation SLAM Neural Rendering (reference)
NeRF/3DGS-based SLAM systems → see Pillar E below
System
Author/Year
Key Concepts
NeRF Mildenhall 2020 Neural Radiance Fields, novel view synthesis (foundational)
DIFIX3D+ Wu 2025 Single-step diffusion for 3D reconstruction artifact removal (post-processing)
System
Author/Year
Key Concepts
EFM3D Straub (Meta) 2024 Egocentric Foundation Model 3D benchmark, depth/surface/semantic from ego-video
Semantic / Language-Grounded SLAM
Also see: LEGS OpenGS-SLAM Open-YOLO 3D
E. Foundation-Model & Neural-Representation SLAM
System
Author/Year
Key Concepts
iMAP Sucar 2021 First NeRF-SLAM, single MLP, real-time tracking/mapping
BARF Lin 2021 Bundle-Adjusting NeRF, coarse-to-fine positional encoding, joint pose+NeRF opt (not full SLAM — pose+NeRF co-optimization)
NICE-SLAM Zhu & Peng 2022 Hierarchical feature grid (coarse/mid/fine), scalable
Co-SLAM Wang 2023 Hash grid (Instant-NGP) + coordinate encoding, 5-10× faster than NICE-SLAM
ESLAM Johari 2023 Tri-plane representation, O(N²) vs O(N³) memory
Point-SLAM Sandström 2023 Neural point cloud based
NeRF-SLAM Rosinol 2023 NeRF + classical SLAM pipeline
NICER-SLAM Zhu 2024 RGB-only NeRF-SLAM (no depth sensor), monocular depth integration
vMAP Kong 2023 Object-level NeRF-SLAM, per-object neural fields
GO-SLAM Zhang 2023 Global optimization + NeRF-SLAM, loop closure + global BA
System
Author/Year
Key Concepts
SplaTAM Keetha 2024 Among the first 3DGS SLAM systems (concurrent with GS-SLAM, MonoGS), RGB-D, silhouette-guided densification
MonoGS Matsuki 2024 First monocular 3DGS SLAM (CVPR 2024 highlight), direct rasterization-based tracking, analytic camera Jacobians
GS-ICP SLAM Ha 2024 Gaussian-to-Gaussian ICP (Mahalanobis distance), geometric tracking
Photo-SLAM Huang 2024 Explicit geometry + implicit appearance (MLP color), anti-aliasing
RTG-SLAM Peng 2024 Real-time focus, adaptive Gaussian budget, Jetson Orin 25 FPS
EGG-Fusion Pan 2025 Geometry-aware Gaussian surfel fusion on the fly, information-filter-based, real-time
Online 3DGS Modeling Lee 2025 Online 3D Gaussian Splatting modeling with novel view selection
ActiveSplat Li 2025 Active mapping with 3DGS + Voronoi-based path planning
OpenGS-SLAM Yang 2025 Open-set dense semantic 3DGS SLAM, object-level scene understanding
LEGS Yu 2024 Language Embedded Gaussian Splats, real-time language-queryable 3D
Tightly-coupled vs Loosely-coupled Filter-based vs Optimization-based IMU preintegration IMU noise model Observability Deployed VIO
System
Author/Year
Key Concepts
OKVIS Leutenegger 2015 Keyframe-based, tightly-coupled, sliding window optimization
VINS-Mono Qin 2018 Tightly-coupled, relocalization, loop closure, pose graph optimization
VI-DSO von Stumberg 2018 Direct sparse VIO, dynamic marginalization, photometric error
VINS-Fusion Qin 2019 Stereo + GPS fusion extension
maplab Schneider 2018 Multi-session visual-inertial mapping framework
Kimera-VIO Rosinol 2020 Fast VIO frontend for Kimera pipeline, structureless vision factors
Basalt Usenko 2020 Non-linear factor recovery (NFR) of marginalization priors, visual-inertial odometry + mapping
ORB-SLAM3 Campos 2020 VIO mode, multi-map, IMU initialization
DM-VIO von Stumberg 2022 Direct (DSO-based) monocular VIO, delayed marginalization, pose-graph BA for IMU initialization
OKVIS2 Leutenegger 2022 Multi-session, improved marginalization
AirVO Xu 2023 Point-line VIO, illumination-robust
OKVIS2-X Boche & Leutenegger 2025 Multi-sensor SLAM (Visual+Inertial+Depth+LiDAR+GNSS), dense volumetric occupancy maps, submapping for large-scale (9km+), EuRoC/Hilti22 SOTA
System
Author/Year
Key Concepts
S-PTAM Pire 2017 Stereo PTAM, ROS-compatible, real-time
ORB-SLAM2 Mur-Artal 2017 Stereo + RGB-D modes, loop closure, relocalization
StereoMSCKF Sun 2018 MSCKF with stereo, efficient for resource-constrained platforms
RTAB-Map Labbé 2019 Multi-sensor (stereo/RGB-D/LiDAR), memory management, large-scale (→ also in Level 4)
ORB-SLAM3 Campos 2020 Multi-map, Atlas, stereo + IMU
Stella-VSLAM Community 2021 OpenVSLAM successor, stereo support (→ also in Level 3)
Stereo DSO Wang 2017 Direct sparse stereo odometry, large-scale (DSO extension)
Level 8: Collaborative / Multi-Robot SLAM Level 9: LiDAR & Visual-LiDAR Fusion SLAM LiDAR / LiDAR-Inertial SLAM
System
Author/Year
Key Concepts
LOAM Zhang 2014 LiDAR odometry and mapping (foundational), edge + planar features
SuMa Behley (Bonn) 2018 Surfel-based LiDAR SLAM, projective ICP on range images
SuMa++ Chen (Bonn) 2019 SuMa + RangeNet++ semantics, semantic ICP weighting, dynamic object filtering
LIO-SAM Shan 2020 Tightly-coupled LiDAR-inertial, factor graph, GPS fusion
FAST-LIO2 Xu 2022 Direct LiDAR-inertial, ikd-Tree, extremely fast
PIN-SLAM Pan (Bonn) 2024 Neural point cloud LiDAR SLAM, point-to-SDF registration, elastic map deformation for loop closure
Level 10: Event Camera SLAM
Event cameras (DVS) Advantages Challenges Event representations
Level 11: World Models & Spatial AI
World model VLM vs VLA Spatial AI
System
Author/Year
Key Concepts
DreamFusion Poole 2023 Text-to-3D via Score Distillation Sampling (SDS) + NeRF
Vision-Language Models (VLM) Vision-Language-Action Models (VLA)
Book
Author
Key Topics
Introduction to Visual SLAM Xiang Gao et al.
VO, optimization, Lie algebra, backend, loop closure — best entry-level SLAM book
Photogrammetric Computer Vision Wolfgang Förstner & Bernhard Wrobel
Camera geometry, estimation, 3D reconstruction — mathematically rigorous
Multiple View Geometry in Computer Vision Richard Hartley & Andrew Zisserman
Epipolar geometry, trifocal tensor, reconstruction — THE bible
Computer Vision: Algorithms and Applications Richard Szeliski
Feature detection, stereo, motion, 3D — comprehensive reference (2nd ed. free PDF)
State Estimation for Robotics Timothy Barfoot
Estimation theory, Lie groups, batch/recursive estimation — free PDF (2nd ed.)
Probabilistic Robotics Thrun, Burgard & Fox
Bayes filters, EKF/particle-filter SLAM — the classical probabilistic foundation
Factor Graphs for Robot Perception Frank Dellaert & Michael Kaess
Factor graphs, elimination, iSAM2 — the backend bible (free PDF)
SLAM Handbook Carlone, Kim, Barfoot, Cremers, Dellaert (eds.)
From localization and mapping to spatial intelligence — free community book (2024-25)
Survey
Author/Year
Key Concepts
Past, Present, and Future of SLAM Cadena 2016 The canonical orientation survey — robust perception age, open problems
Event-based Vision Survey Gallego 2020 Event cameras and algorithms (→ also in Level 10)
Resource
Link
SLAM Zero-to-Hero code exercises GitHub — Docker-based hands-on exercises for this roadmap's topics (feature detection, epipolar geometry, RANSAC, ICP, g2o/GTSAM/Ceres) and systems (ORB-SLAM2, Basalt, Kimera, FAST-LIO2, MASt3R-SLAM, ...); individual exercises are linked from the matching study notes
changh95/slam_lecture_codes GitHub — Full SLAM lecture code collection
If you think any part of the roadmap can be improved, please open a PR or submit an issue. I will continue to improve this, so you may want to watch/star this repository and revisit it later.
Also, check out my GitHub and blog 😺
Open pull request with improvements
Discuss ideas in issues
Spread the word
Reach out to me directly at hyunggi.chang95[at]gmail.com
To discuss any topics or ask questions, please use the issue tab .
This project is licensed under the MIT License :
Copyright © 2026 Hyunggi Chang .
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
