tackling 3d tof artifacts through learning and the … · 2019-03-29 · 3 tackling 3d tof...
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Iuri Frosio, GTC 2019 (San Jose, CA)
TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
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THE IMPORTANCE OF NEGATIVE RESULTS
“I shall require that [the] logical form [of thetheory] shall be such that it can be singledout, by means of empirical tests, in anegative sense: it must be possible for anempirical scientific system to be refuted byexperience” (Karl Popper, The Logic ofScientific Discovery, 1959).
In simple words, “negative results arefundamentals for the advancement ofscience”.
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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TIME OF FLIGHT (TOF) CAMERAS & ARTIFACTSE.g., Kinect 2
Image from https://stackoverflow.com/questions/22921390/how-to-scale-a-kinect-
depth-image-for-applying-lbp-on-it-in-matlab?rq=1
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TIME OF FLIGHT (TOF) CAMERAS & ARTIFACTSApplications
Image from https://www.physio-
pedia.com/The_emerging_role_of_Microsoft_Kinect_in_physiotherapy_rehabilitation_for_stroke_patients
Image from amazon.com
Image from https://www.eenewsautomotive.com/news/3d-lidar-generates-environmental-model-time-flight-
measurement
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TIME OF FLIGHT (TOF) CAMERAS & ARTIFACTSArtifact #1: shot noise
Image from https://ptgrey.com
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TIME OF FLIGHT (TOF) CAMERAS & ARTIFACTSArtifact #2: movement
Image from https://support.xbox.com
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TIME OF FLIGHT (TIF) CAMERAS & ARTIFACTSArtifact #3: multiple reflections
Image from
https://www.sciencedirect.com/science/article/pii/S0262885613001650
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TIME OF FLIGHT (TIF) CAMERAS & ARTIFACTSArtifact #3: multiple reflections
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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NAÏVE MACHINE LEARNING (ML) FOR TOF RECONSTRUCTION
What do we need?
(1) A large dataset of scenes…
(2) … corrupted by:
(1.1) photon noise,(1.2) motion,(1.3) multiple reflections…
(3) … with clean output data…
(4) … And a DNN.
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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TOF WORKING PRINCIPLESTime of flight is not time of flight ☺
Image from https://www.semanticscholar.org/paper/Interference-mitigation-technique-for-(ToF)-camera-Islam-Hossain/
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TOF WORKING PRINCIPLESTime of flight is not time of flight ☺
Images from https://www.semanticscholar.org/paper/Interference-mitigation-technique-for-(ToF)-camera-Islam-Hossain/
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TOF WORKING PRINCIPLESMultiple measurements
Pulse method
Continuous wave method
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TOF WORKING PRINCIPLESCamera functions and scene response
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TOF WORKING PRINCIPLEMore on scene response
= න−𝑇2
𝑇2𝑓 𝑡 ⨂𝑔𝑖 𝑡 𝑟 𝑡 𝑑𝑡= න
−𝑇2
𝑇2𝑓 𝑡 ∗ 𝑟 𝑡 𝑔𝑖 𝑡 𝑑𝑡Raw measurement:
Emitted signal: 𝑓 𝑡
Returned signal: ℎ 𝑡
Demodulation signal: 𝑔𝑖 𝑡
Impulse response: 𝑟(𝑡)
Camera Scene
𝑄𝑖(𝑡) = න−𝑇2
𝑇2ℎ 𝑡 𝑔𝑖 𝑡 𝑑𝑡
Depth: 𝑍 =𝑇𝑐
4𝜋arctan
σ𝑖 sin𝜃𝑖 𝑄𝑖 𝑡
σ𝑖 cos𝜃𝑖 𝑄𝑖 𝑡
t
t
tDemodulation: gi(𝑡)
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TOF WORKING PRINCIPLESMultiple frequencies
• Different max length (combinethem)
• Different resolutions
• Agreement between differentmeasurements
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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CAMERA CALIBRATIONCamera response functions (flat scene)
Inside coated with black-out material
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CAMERA CALIBRATIONCamera response functions (flat scene)
• Three “frequencies”
• Three measurements perfrequency
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CAMERA CALIBRATIONPhoton noise
Other calibration details (pixel delay, vignetting, … in Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLAT Dataset, ECCV 2018, Munich (Germany), Sept. 2018.
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
To disk (FLAT)https://github.com/NVlabs/FLAT
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
Transient rendering (scene response function) based on Jarabo, A., Marco, J., Muñoz, A.,Buisan, R., Jarosz, W., Gutierrez, D.: A framework for transient rendering. In: ACMTransactions on Graphics (SIGGRAPH ASIA), (2014).
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
Transient rendering (scene response function) based on Jarabo, A., Marco, J., Muñoz, A.,Buisan, R., Jarosz, W., Gutierrez, D.: A framework for transient rendering. In: ACMTransactions on Graphics (SIGGRAPH ASIA), (2014).
Brightness, travel time
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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Impulse response
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
Different cameras (beyond Kinect 2) can be simulated, after calibration.
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
Noise can be added…
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
… As well as motion (approximate model) and texture…
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THE FLAT DATASETFlexible, Large, Augmentable, ToF (FLAT)
… and multiple reflections.
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THE FLAT DATASETSamples
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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NAÏVE MACHINE LEARNING (ML) FOR TOF RECONSTRUCTION
Supervised learning
Take it easy: supervised learning, from raw data to 3D map.
Training input/output pairs from the FLAT dataset.
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NAÏVE MACHINE LEARNING (ML) FOR TOF RECONSTRUCTION
Supervised learning
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THE LESSON WE LEARNED*…* To advance science.
That’s a nice negative result!
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… AND HOW WE IMPROVED
*1 Yes, it’s a fakepicture…
*2 … But the message iscorrect.
*1
*2
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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BACK TO PHYSICS
Cause: Sequential measurements
Effect: Misaligned moving object
Solution:Warping
And, more generally speaking, any a-priori knowledge.
time
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BACK TO PHYSICS
Cause: DNN architecture andlearningmocks physics
Effect: Sub-optimal results
Solution:Include physics in the DNNarchitecture / reconstructionpipeline.
And, more generally speaking, any a-priori knowledge.
Naïve machine learning
Physics
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DNN ARCHITECTURE#1: Motion Correction Module
Trained to warp imagesto the central one
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DNN ARCHITECTURE#2: Motion Reflection Module
Trained to reducemultiple reflection afterre-alignment.
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DNN ARCHITECTURE#3: Differential reconstruction pipeline
Non-trainable, butdifferentiable,physics-based reconstructionpipeline.
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DNN ARCHITECTURE#3: Differential reconstruction pipeline
Can be refined withend-to-end training.
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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131NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
RESULTS
Compare against
LF2 [1] [Kinect, non DL]
DToF [3] [DL, Raw to 3D, no motion]
Phasor [2] [High frequencies reduce MPI]
[1] Xiang, et al. libfreenect2: Release 0.2[2] Marco, et al. DeepToF: Off-the-shelf real -time correction of multipath interference in time-of-flight imaging. In: ACM Transactions on Graphics (SIGGRAPH ASIA).[3] Gupta, et al. Phasor imaging: A generalization of correlation-based time-of-flight imaging. ACM Transactions on Graphics.
Competitors & ablation study
Ablation study
MOM [motion only]
MRM [multiple reflection and noise only]
MOM-MRM [motion, multiple reflection and noise ]
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RESULTSAblation study: none, MRM, MOM+MRM [simulation]
Median [Med] and Inter Quartile Range [IQR] of the error decreased by MRM / MOM-MRM, in cm.
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RESULTSAblation study: none, MRM, MOM+MRM [simulation]
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RESULTSCompare against: DTOF, Phasor imaging [simulation]
Smaller error when compared to DToF or Phasor.
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RESULTSCompare against: DTOF, Phasor imaging on multi-reflection and shot
noise [simulation]
Field of view
Multiple reflection removed through local reflection / a-priori information, no bias.
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RESULTSCompare against: LF2, on multi-reflection and shot noise [real data]
Multiple reflection removed through local reflection / coherence / a-priori information, no bias.
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RESULTSCompare against: LF2, on movement [real data]
Realignment of raw data reduce motion artifacts, specular reflections (red box) generate errors.
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TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE FLAT DATASET
- Time Of Flight (TOF) cameras & artifacts
- Naïve Machine Learning (ML) for TOF reconstruction
- TOF cameras: working principles
- Camera calibration
- The FLAT dataset
- Spoiler: our non-Naïve ML solution works*
- Back to physics
- DNN architecture
- Results
- Conclusion
Agenda
* See Qi Guo, Iuri Frosio, Orazio Gallo,Todd Zickler, Jan Kautz, Tackling 3D ToFArtifacts Through Learning and the FLATDataset, ECCV 2018, Munich (Germany),Sept. 2018.
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CONCLUSION
1. Naïve ML does not always work…
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CONCLUSION
1. Naïve ML does not always work…
2. ….But going back to a priori knowledge may help.
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CONCLUSION
The physics of ToF cameras: acquisition, reconstruction, artifacts
Photon shot noise, motion artifacts, multiple reflection
A large dataset of simulated data
Design the DNN architecture accordingly to a-priori knowledge
Effective reduction of reconstruction artifacts
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RESOURCES
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset, Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, Jan Kautz; The European Conference on Computer Vision (ECCV), 2018, pp. 368-383, http://openaccess.thecvf.com/content_ECCV_2018/html/Qi_Guo_Tackling_3D_ToF_ECCV_2018_paper.html
The FLAT dataset (code and data): https://github.com/NVlabs/FLAT
Contact: {ifrosio, ogallo}@nvidia.com, [email protected]
Online