3D Scene Understanding from RGB-D Images. Thomas Funkhouser
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1 3D Scene Understanding from RGB-D Images Thomas Funkhouser
2 Recent Ph.D. Student Current Postdocs Current Ph.D. Students Disclaimer: I am talking about the work of these people Shuran Song Yinda Zhang Andy Zeng Maciej Halber Kyle Genova Fisher Yu Manolis Savva Angel Chang
3 Motivation Help devices with RGB-D cameras understand their 3D environments Robot manipulation Augmented reality Virtual reality Personal assistance Surveillance Navigation Mapping Games etc.
4 Depth (D) Color (RGB) Goal Given a RGB-D image, infer a complete, annotated 3D representation Wall Picture Nightstand Pillow Nightstand Bed Door Bench Free space Wall Input: RGB-D Image Output: complete, annotated 3D representation
5 Problem Challenge: get only partial observation of scene, must infer the rest Input: RGB-D Image Side view
6 Problem Challenge: get only partial observation of scene, must infer the rest Input: RGB-D Image Rotating side view
7 Problem Challenge: get only partial observation of scene, must infer the rest Input: RGB-D Image Top view
8 Problem Challenge: get only partial observation of scene, must infer the rest Beyond Field of View Input: RGB-D Image Top view
9 Problem Challenge: get only partial observation of scene, must infer the rest Beyond Field of View Occluded Regions Input: RGB-D Image Top view
10 Problem Challenge: get only partial observation of scene, must infer the rest Beyond Field of View Occluded Regions Missing Depths Input: RGB-D Image Top view
11 Problem Challenge: get only partial observation of scene, must infer the rest Beyond Field of View Input: RGB-D Image Missing Depths Top view Structure Free space Occluded Regions
12 Problem Challenge: get only partial observation of scene, must infer the rest Beyond Field of View Wall Picture Semantics Nightstand Pillow Nightstand Bed Occluded Regions Missing Depths Bench Free space Wall Door Structure Input: RGB-D Image Top view
13 Talk Outline Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work
14 Talk Outline (Part 1) Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work Yinda Zhang and Thomas Funkhouser, Deep Depth Completion of a Single RGB-D Image, CVPR 2018 (spotlight on Tuesday)
15 Deep Depth Completion Goal: estimate depths missing from an RGB-D image Color (RGB) Output Depth (D) Raw Depth (D)
16 Deep Depth Completion Goal: estimate depths missing from an RGB-D Thin image Structures Shiny Surfaces Distant Surfaces Bright illumination Color (RGB) Black Surfaces Missing Depth Raw Depth (D) from Intel R200 camera
17 Deep Depth Completion Motivation: help upstream applications understand 3D environment Raw Depth Output Depth RGB-D images shown as colored 3D point clouds
![2016] Harmonizing](/docs-images/87/96929619/images/18-7.jpg "Overcomplete")
18 Deep Depth Completion Previous work on depth completion (from RGB-D): Joint Bilateral Filter [Silberman, 2012] Previous work on depth estimation (from RGB): Sparsity Invariant CNNs [Uhrig, 2017] Deeper Depth Prediction [Laina, 2016] Harmonizing Overcomplete Predictions [Chakrabarti, 2016]
19 Deep Depth Completion Problem: estimating depth from color requires global scene understanding FCN Input Color Output Depth
20 Deep Depth Completion Approach: estimate local surface normals from color, and then solve for depths globally with system of equations FCN System of Equations Input Color Surface Normals Output Depth Input Depth
21 Deep Depth Completion Rationale 1: estimating surface normals is easier than estimating depths Constant within planar regions Determined by local shading (for diffuse surfaces) Often associated with specific textures Color Estimated Surface Normals Y. Zhang, S. Song, E. Yumer, M. Savva, J.-Y. Lee, H. Jin, T. Funkhouser, Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks, CVPR 2017
22 Deep Depth Completion Rationale 2: depths can be estimated robustly from normals Solution is unique for each continuously connected component (up to scale) N(p) p r q Non-linear system of equations: N(p) = (v(p,q) x v(p,r))/ (v(p,q) x v(p,r)) Linear approximation: N(p) v(p,q) = 0 N(p) v(p,r) = 0
23 Deep Depth Completion Rationale 2: depths can be estimated robustly from normals Solution is unique for each continuously connected component (up to scale) N(p) p r q
24 Deep Depth Completion Rationale 2: depths can be estimated robustly from normals Real-world scenes generally have few (one) continuously connected components
25 Deep Depth Completion Rationale 2: depths can be estimated robustly from normals We use observed depths and smoothness constraints to guarantee a solution N(p) p r q
26 Deep Depth Completion Rationale 2: depths can be estimated robustly from normals Solving the linearized equations guarantees a globally optimal solution FCN Linear System of Equations Input Color Surface Normals Output Depth Input Depth
27 Deep Depth Completion: Data Where get real training/test data? Missing Depth Color Raw Depth
28 Deep Depth Completion: Data Where get real training/test data? Complete depths by rendering RGB-D SLAM surface reconstructions (ScanNet, Matteport3D) Color Raw Depth ScanNet Surface Reconstruction A. Dai, A.X. Chang, M. Savva, M. Halber, T. Funkhouser, M. Niessner., ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes, CVPR 2017
29 Deep Depth Completion: Data Where get real training/test data? Complete depths by rendering RGB-D SLAM surface reconstructions (ScanNet, Matteport3D) Color Raw Depth ScanNet Surface Reconstruction A. Dai, A.X. Chang, M. Savva, M. Halber, T. Funkhouser, M. Niessner., ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes, CVPR 2017
30 Deep Depth Completion: Data Where get real training/test data? Complete depths by rendering RGB-D SLAM surface reconstructions (ScanNet, Matteport3D) Color Raw Depth Rendered Depth ScanNet Surface Reconstruction A. Dai, A.X. Chang, M. Savva, M. Halber, T. Funkhouser, M. Niessner., ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes, CVPR 2017
![Deep Depth Completion: Results Comparisons to other depth completion methods: [5] J. T. Barron and B. Poole. The fast bilateral solver. ECCV 2016. [6] D.](/docs-images/87/96929619/images/31-0.jpg "Garcia. Robust smoothing of gridded data in one and higher dimensions with missing values. Comp. stat. & data anal., 2010. [13] Y. Zhang et al.")
![Physically-based rendering for indoor scene understanding using convolutional neural networks. CVPR 2017. [20] D. Ferstl et al.](/docs-images/87/96929619/images/31-2.jpg "Image guided depth upsampling using anisotropic total generalized variation. ICCV 2013. [64] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus.")
31 Deep Depth Completion: Results Comparisons to other depth completion methods: [5] J. T. Barron and B. Poole. The fast bilateral solver. ECCV [6] D. Garcia. Robust smoothing of gridded data in one and higher dimensions with missing values. Comp. stat. & data anal., [13] Y. Zhang et al. Physically-based rendering for indoor scene understanding using convolutional neural networks. CVPR [20] D. Ferstl et al. Image guided depth upsampling using anisotropic total generalized variation. ICCV [64] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. ECCV 2012.
![Deep Depth Estimation: Results Comparison to other depth estimation methods: Laina [37] Chakr. [7] Laina [37] Chakr. [7] [7] Chakrabarti, A. et al.](/docs-images/87/96929619/images/32-0.jpg ", Depth from a single image by harmonizing overcomplete local network predictions. NIPS 2016. [37] Laina, C. et al.")
32 Deep Depth Estimation: Results Comparison to other depth estimation methods: Laina [37] Chakr. [7] Laina [37] Chakr. [7] [7] Chakrabarti, A. et al., Depth from a single image by harmonizing overcomplete local network predictions. NIPS [37] Laina, C. et al., Deeper depth prediction with fully convolutional residual networks. 3DV 2016.
33 Deep Depth Completion: Results Intel RealSense R200 examples: Color Image Sensor Depth Completed Depth Sensor Point Cloud Completed Point Cloud
34 Deep Depth Completion: Results Intel RealSense R200 examples: Color Image Sensor Depth Completed Depth Sensor Point Cloud Completed Point Cloud
35 Talk Outline (Part 2) Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work Shuran Song, Fisher Yu, Andy Zeng, Angel Chang, Manolis Savva, and Thomas Funkhouser, Semantic Scene Completion from a Single Depth Image, CVPR 2017 (oral)
36 Semantic Scene Completion Goal: estimate the semantics and geometry occluded from a depth camera RGB-D Image Input: Single view depth map Output: Semantic scene completion
37 Semantic Scene Completion Formulation: given a depth image, label all voxels by semantic class visible surface free space occluded space outside view outside room 3D Scene
38 Semantic Scene Completion Formulation: given a depth image, label all voxels by semantic class visible surface free space occluded space outside view outside room 3D Scene
39 Semantic Scene Completion Prior work: segmentation OR completion surface segmentation Silberman et al. scene completion Firman et al. 3D Scene The occupancy and the object This identity paper are tightly intertwined! semantic scene completion
40 Semantic Scene Completion Approach: end-to-end 3D deep network Prediction: N+1 classes SSCNet Input: Single view depth map Output: Volumetric occupancy + semantics Simultaneously predict voxel occupancy and semantics classes by a single forward pass.
41 Semantic Scene Completion: Network Architecture
42 Semantic Scene Completion: Network Architecture
43 Semantic Scene Completion: Network Architecture Voxel size: 0.02 m
44 Semantic Scene Completion: Network Architecture Voxel size: 0.02 m View Standard TSDF
45 Semantic Scene Completion: Network Architecture Voxel size: 0.02 m View Standard TSDF Flipped TSDF Encode 3D space using flipped TSDF
46 Semantic Scene Completion: Network Architecture Voxel size: 0.02 m Receptive field: 0.98 m Receptive field:1.62 m Receptive field: 2.26 m Extract features for different physical scales
47 Semantic Scene Completion: Network Architecture receptive field learnable parameter Receptive Field = 7x7x7 Parameters = 27 Larger receptive field with same number of parameters and same output resolution! Dilated Convolutions F. Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions, ICLR 2016
48 Semantic Scene Completion: Data Where get training data? NYUv2 Small number of objects labeled with CAD models (suitable for testing, not training) N. Silberman, P. Kohli, D. Hoiem, R. Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV 2012 R. Guo, C. Zou, D. Hoiem, Predicting Complete 3D models of Indoor Scenes, arxiv 2015
49 Semantic Scene Completion: Data SUNCG dataset 46K houses 50K floors 400K rooms 5.6M object instances
50 Semantic Scene Completion: Data SUNCG dataset synthetic camera views depth ground truth semantic scene completion
51 Semantic Scene Completion: Experiments Pre-train on SUNCG Fine-tune and test on NYUv2
52 Semantic Scene Completion: Results Input Color Our Result Ground Truth Input Depth
53 Semantic Scene Completion: Results Input Color Our Result Ground Truth Input Depth
54 Semantic Scene Completion: Results Result 1: better than previous volumetric completion algorithms Comparison to previous algorithms for volumetric completion
55 Semantic Scene Completion: Results Result 2: better than previous semantic labeling algorithms Comparison to previous algorithms for semantic labeling with 3D model fitting
56 Talk Outline (Part 3) Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, and Thomas Funkhouser, Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View, CVPR 2018 (oral)
57 Semantic View Extrapolation Goal: given an RGB-D image, predict 3D structure and semantics outside view 360 Output 1: 3D structure ceiling ceiling Input: RGB-D Image door nightstand chair Bed Bed floor Output 2: semantic segmentation
58 Semantic View Extrapolation Input: RGB-D Image
59 Semantic View Extrapolation Input: RGB-D Image Output: 360 panorama with 3D structure & semantics Nightstand Bed Window 360 Wall
60 Semantic View Extrapolation Prior work: extrapolating appearance (color) outside field of view Pathak et al. CVPR 2017
61 Semantic View Extrapolation Our work: predicting 3D structure and semantics for full 360 panorama 360 3D structure ceiling ceiling door nightstand chair Bed floor Semantic segmentation Bed
62 Semantic View Extrapolation 3D structure representation: plane equation per pixel (normal and offset) Plane Equation ax + by + cz - d=0 (a,b,c) = normal d = plane offset from origin Similar to first project
63 Semantic View Extrapolation: Network Architecture Scene attribute losses: Scene category Object distribution Pixel-wise loss Adversarial loss
64 Semantic View Extrapolation: Training Objectives
65 Semantic View Extrapolation: Training Objectives Every pixel is correct Prediction Hard for even humans to do. Lose the ability to generalize. Ground truth
66 Semantic View Extrapolation: Training Objectives Every pixel is correct Prediction is plausible G:generator D: discriminator Real or fake Prediction Adversarial loss Goodfellow et al. 2014
67 wall floor ceiling chair wall floor ceiling chair Semantic View Extrapolation: Training Objectives Every pixel is correct Similar scene attributes Prediction is plausible Scene Category Object Distribution Prediction Ground truth
68 Semantic View Extrapolation: Training Objectives Every pixel is correct Similar scene attribute Prediction is plausible Scene Category Object Distribution Prediction Ground truth
69 Semantic View Extrapolation: Training Objectives Every pixel is correct Similar scene attribute Prediction is plausible
70 Semantic View Extrapolation: Network Architecture Scene attribute losses: Scene category Object distribution Pixel-wise loss Adversarial loss
71 Semantic View Extrapolation: Data Where get training/test data? 3D structure ceiling ceiling door nightstand chair Bed floor Semantic segmentation Bed
72 Semantic View Extrapolation: Data Matterport3D dataset Matterport Camera 3D Building Reconstruction A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, Y Zhang, Matterport3D: Learning from RGB-D Data in Indoor Environments, 3DV 2017
73 Semantic View Extrapolation: Data Matterport3D dataset Matterport Camera 3D Building Reconstruction A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, Y Zhang, Matterport3D: Learning from RGB-D Data in Indoor Environments, 3DV 2017
74 Semantic View Extrapolation: Data Matterport3D dataset Matterport Camera RGB-D Panorama with Semantics 3D Building Reconstruction A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, Y Zhang, Matterport3D: Learning from RGB-D Data in Indoor Environments, 3DV 2017
75 Semantic View Extrapolation: Experiments Pre-train on SUNCG 58,866 synthetic panoramas Fine-tune and test on Matterport3D 5,315 real panoramas
76 Semantic View Extrapolation: Results Input Observation
77 Semantic View Extrapolation: Results Ceiling Prediction Floor Wall Bed
78 Semantic View Extrapolation: Results Prediction Ground truth Bed Window Object
79 Semantic View Extrapolation: Results Prediction Ground truth Bed Window Object
80 Semantic View Extrapolation: Results Prediction Ground truth Bed Window Object
81 Semantic View Extrapolation: Results Prediction Ground truth Bed Window Object
82 Semantic View Extrapolation: Results Prediction Ground truth Bed Window Object
83 Semantic View Extrapolation: Results Comparison to alternative completion methods Ours 0.11 Nearest Two-Step Input Ours 0 Semantic Accuracy (IoU) Nearest Two-Step Image Inpainting Two Step Approach Ours 0 3D Structure Error (L2)
84 Summary Scene understanding from partial observation Wall Picture Semantics Nightstand Pillow Nightstand Bed Input: RGB-D Image Door Bench Structure Free space Output: complete, annotated 3D representation Wall
85 Talk Outline Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work
86 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed
87 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed
88 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed Surface Normals Flipped TSDF Plane Equations
89 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed
90 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed Global Solution to Linear System of Equations Dilated Convolutions Panoramic Representations
91 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very important even for simply estimating depth Can leverage larger contexts with global minimization, dilated convolutions, etc. 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed
92 Common Themes Geometric representation Choice of 3D representation is critical Choosing the most obvious representation is usually not best Large-scale context Global context is very Multiroom important even SUNCG for simply Matterport3D estimating depth SUN3D Can leverage larger contexts with global minimization, dilated convolutions, etc. Largest 3D datasets available today for indoor environments 3D Dataset curation Synthetic 3D datasets very useful for training Real 3D datasets are important for testing. More needed Synthetic RGB-D Image RGB-D Video Object ShapeNet Intel RealSense Redwood Room SUNCG SUN RGB-D ScanNet
93 Talk Outline Introduction Three recent projects Deep depth completion [CVPR 2018] Semantic scene completion [CVPR 2017] Semantic view extrapolation [CVPR 2018] Common themes Future work
94 Future work Large-scale scenes Self-supervision Active sensing
95 Acknowledgments Princeton students and postdocs: Angel X. Chang, Kyle Genova, Maciej Halber, Manolis Savva, Elena Sizikova, Shuran Song, Fisher Yu, Yinda Zhang, Andy Zeng Google collaborators: Martin Bokeloh, Alireza Fathi, Sean Fanello, Aleksey Golovinskiy, Shahram Izadi, Sameh Khamis, Adarsh Kowdle, Johnny Lee, Christoph Rhemann, Jurgen Sturm, Vladimir Tankovich, Julien Valentin, Stefan Welker Other collaborators: Angela Dai, Vladlen Koltun, Matthias Niessner, Alberto Rodriquez, Silvio Savarese, Yifei Shi, Jianxiong Xiao, Kai Xu Data: SUN3D, NYU, Trimble, Planner5D, Matterport Funding: NSF, Google, Intel, Facebook, Amazon, Adobe, Pixar Thank You!
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