TACKLING 3D TOF ARTIFACTS THROUGH LEARNING AND THE … · 2019-03-29 · 3 TACKLING 3D TOF...

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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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.

130

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.

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, qiguo@g.harvard.edu

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