Large-Scale Point Cloud Classification Benchmark
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1 Large-Scale Point Cloud Classification Benchmark IGP & CVG, ETH Zürich 7/6/2016 1
2 Timo Hackel Nikolay Savinov Ľubor Ladický Jan Dirk Wegner Konrad Schindler Marc Pollefeys People IGP & CVG, ETH Zürich 7/6/2016 2
3 ~ $ annually spend for terrestrial laser scanners Measure up to D points per second Laser scanner Terrestrial Laser Scanning Architecture, Archaeology, City Modelling, Mining, Monitoring, IGP & CVG, ETH Zürich info@semantic3d.net 7/6/2016 3
4 Objective Assign a class label to each 3D point individually IGP & CVG, ETH Zürich info@semantic3d.net 7/6/2016 4
![iqmulus & TerraMobilita NYU Related Benchmark Tests [1] Vallet, Bruno, et al.](/docs-images/90/102186177/images/5-0.jpg "\"TerraMobilita/iQmulus urban point cloud analysis benchmark.\" Computers & Graphics 49 (2015): 126-133.")
![[2] Silberman, Nathan, et al. "Indoor segmentation and support inference from RGBD images.](/docs-images/90/102186177/images/5-1.jpg "\" Computer Vision ECCV 2012. Springer Berlin Heidelberg, 2012. 746-760. IGP & CVG, ETH Zürich www.")
5 iqmulus & TerraMobilita NYU Related Benchmark Tests [1] Vallet, Bruno, et al. "TerraMobilita/iQmulus urban point cloud analysis benchmark." Computers & Graphics 49 (2015): [2] Silberman, Nathan, et al. "Indoor segmentation and support inference from RGBD images." Computer Vision ECCV Springer Berlin Heidelberg, IGP & CVG, ETH Zürich 7/6/2016 5
6 IGP & CVG, ETH Zürich 7/6/2016 6
7 Data {x, y, z, intensity, r, g, b} IGP & CVG, ETH Zürich 7/6/2016 7
8 Intensity External illumination sensor used to remove external illumination (sun, etc.) Value range: to IGP & CVG, ETH Zürich info@semantic3d.net 7/6/2016 8
9 Color Color was recorded after scanning IGP & CVG, ETH Zürich 7/6/2016 9
10 Cube maps IGP & CVG, ETH Zürich 7/6/
11 Scanning Artefacts IGP & CVG, ETH Zürich 7/6/
12 Classes Man made terrain Natural terrain High vegetation Low vegetation Buildings Clutter (hard scape) Scanning artefacts Cars IGP & CVG, ETH Zürich 7/6/
13 training and test set Over 3 billion labelled points IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
14 Concept reduced-8 reduced challenge semantic-8 full challenge test set: points 3 scans voxel grid filter: 1 cm resolution training set: points 15 scans unfiltered test set: points 15 scans unfiltered IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
15 How to submit your results: ww.semantic3d.net 1. Create zip file containing the results as described in submit 2. login Create a new classifier 4. Upload data Choose if you want to make results public IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
16 Train, million Test, million Class distribution IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
17 Main: intersection over union (IoU) Averaged over all classes Well-established for segmentation: e.g. PASCAL VOC If c ij = count [class i predicted as j] then: i j i k Auxiliary measure: accuracy Evaluation measures IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
18 (intersection over union) Human labeller disagreement 4% IGP & CVG, ETH Zürich info@semantic3d.net 7/6/
19 Pure Color Deep Learning Covariance Features Baseline methods IGP & CVG, ETH Zürich 7/6/
20 Baseline Pure Color IGP & CVG, ETH Zürich 7/6/
21 Cube mapping IGP & CVG, ETH Zürich Timo Hackel, 7/6/
22 Result Reduced challenge Full challenge IGP & CVG, ETH Zürich Timo Hackel, 7/6/
23 3D Convolutional Neural Network Goal: classify center of neighbourhood (sliding 3D window) Voxel occupancy grid: any point inside voxel? (0/1) Semantic label Baseline II Deep Learning on voxelized neighbourhood IGP & CVG, ETH Zürich 7/6/
24 Prior work: Classification & detection, no segmentation 3D ShapeNets [CVPR 15] VoxNet [IROS 15] Deep Sliding Shapes [CVPR 16] Baseline II Deep Learning on voxelized neighbourhood IGP & CVG, ETH Zürich 7/6/
25 Voxelization details 16x16x16 voxels neighbourhood 5 scales considered: voxel size 2.5 cm m Scales concatenated Batch constructed on C++ side Batch transferred to Torch via Luajit occupancy hash table (sparse) classified x, y, z query neighbourhood (dense) IGP & CVG, ETH Zürich Timo Hackel, timo.hackel@geod.baug.ethz.ch 7/6/
26 Deep net details VGG-like Multi-scale Torch VGG 3x3x3 kernel Input scale 1 Input scale 5 VGG-like 1 VGG-like 5 Concatenation of FC FC Predictions IGP & CVG, ETH Zürich Timo Hackel, timo.hackel@geod.baug.ethz.ch 7/6/
27 Training details & results xy-rotation augmentations necessary x y z direction aligned with gravity Batch size 100, every 100 batches random xy-rotation Trained on 1 point cloud with 259 million points Classes sampled randomly with equal probabilities Test results: DeepNet better than 2D color classification z IGP & CVG, ETH Zürich Timo Hackel, timo.hackel@geod.baug.ethz.ch 7/6/
28 Code available soon! C++/Lua You could build your algorithm on top of that. Will be on benchmark website. Subscribe to our newsletter! IGP & CVG, ETH Zürich Timo Hackel, 7/6/
29 X=2 Y=3.1 Y=1.5 X RF Y p1 p2 p3 p4 compute neighborhoods extract features classify semantically Baseline III traditional machine learning with multiscale features IGP & CVG, ETH Zürich Timo Hackel, timo.hackel@geod.baug.ethz.ch 7/6/
30 Neighborhood approximation KD-trees slow Further approximation needed Multiscale voxel-grid filtering KD-tree pyramid IGP & CVG, ETH Zürich Timo Hackel, 7/6/
31 Feature extraction Spherical neighborhood Cylindrical neighborhood IGP & CVG, ETH Zürich Timo Hackel, 7/6/
32 Training class frequencies per scan do not necessarily represent prior distribution over class labels Error weights depend on distance to scanner Subsampling when scanner origin unknown 1. Subsampling of training set x to generate x 2. Grid search and cross-validation depth d of random forest 3. Train random forest using d and x 4. Detect and remove unused features IGP & CVG, ETH Zürich Timo Hackel, timo.hackel@geod.baug.ethz.ch 7/6/
33 Classification & Implementation details Precompute KD-Trees and keep them in RAM Evaluate features on the fly (nearly no RAM needed) Implementation in C++ with OpenMP Solve eigenvalues and eigenvectors of 3x3 Matrix analytically IGP & CVG, ETH Zürich Timo Hackel, 7/6/
34 Results on Mobile Mapping Data IGP & CVG, ETH Zürich Timo Hackel, 7/6/
35 Results on Mobile Mapping Data IGP & CVG, ETH Zürich Timo Hackel, 7/6/
36 Result Reduced challenge Full challenge IGP & CVG, ETH Zürich Timo Hackel, 7/6/
37 Questions and answers Why does the confusion matrix not contain all points from the submitted result? Why ASCII and not LAZ? Why is the training set of semantic-8 and reduced-8 the same? What are the next steps? IGP & CVG, ETH Zürich 7/6/
38 Demo of baseline III IGP & CVG, ETH Zürich 7/6/
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