Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference

As continuous-wave time-of-flight (C-ToF) cameras become popular in 3D imaging applications, they need to contend with the problem of multi-camera interference (MCI). In a multi-camera environment, a ToF camera may receive light from the sources of other cameras, resulting in large depth errors. In this paper, we propose stochastic exposure coding (SEC), a novel approach for mitigating. SEC involves dividing a camera’s integration time into multiple slots, and switching the camera off and on stochastically during each slot. This approach has two benefits. First, by appropriately choosing the on probability for each slot, the camera can effectively filter out both the AC and DC components of interfering signals, thereby mitigating depth errors while also maintaining high signal-to-noise ratio. This enables high accuracy depth recovery with low power consumption. Second, this approach can be implemented without modifying the C-ToF camera’s coding functions, and thus, can be used with a wide range of cameras with minimal changes. We demonstrate the performance benefits of SEC with theoretical analysis, simulations and real experiments, across a wide range of imaging scenarios.


Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference

Jongho Lee, Mohit Gupta

Proc. ICCV 2019

oral presentation

Smart Time-Multiplexing of Quads Solves the Multicamera Interference Problem

T. Pribanic, T. Petkovic, David Bojanic, K. Bartol, Mohit Gupta

Proc. International Conf. on 3D Vision (3DV) 2020

Multi-Camera interference in C-ToF imaging

When several C-ToF cameras capture the same scene concurrently, each sensor may receive light from the sources of other cameras. This interfering signal prevents correct depth estimation, resulting in potentially large, structured errors.

Multi-camera interference and interference reduction in C-ToF imaging

(a) In C-ToF imaging, depths are recovered from the phases of the measured waveforms. (b) If there are multiple cameras, interfering sources corrupt the measured waveforms, resulting in systematic depth errors. (c) Conventional MCI reduction approaches (ACO) reduce systematic errors by removing AC interference, but DC interference remains, resulting in lower SNR and random depth errors due to higher photon noise. (d) Our approaches (SEC and CMB) mitigate both AC and DC interference, thus reducing both systematic and random depth errors.

Concept of stochastic exposure coding (SEC)

A frame, the most basic unit to estimate
the depth, is divided into M number of slots. Each slot is activated with a probability p. A depth value is estimated from non-clashed ON (activated) slots.

Layered C-ToF coding

The proposed approach operates in the exposure coding layer, where the camera and the source are modulated at micro/millisecond time scales. In contrast, existing MCI reduction approaches operate in the lower depth coding layer, where modulation is performed at nanosecond time scales.

3-D model reconstruction over different number of interfering cameras

Our approaches achieve better performance in both subjective and objective quality over different number of interfering cameras N. The RMSE values (in mm) are shown.

Hardware prototype

Front and top views of our setup to implement ACO, SEC, and CMB. The setup consists of four C-ToF cameras and four microcontrollers to generate random binary sequences to activate the cameras by given slot ON probabilities.

Depth estimation comparison over different energy consumption

Our approaches show better performance at lower energy consumption than the conventional approach. The % of inliers (non-black pixels) and RMSE values (in m) at the inliers are represented for comparison between approaches.


Presentation Slides


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