StereoSync: Synchronization in Stereo Systems

• StereoSync is a unifying concept for systems that integrate temporal synchronization and stereo processing across domains like event-based vision and audio reproduction.
• It encompasses methods that temporally warp image events, calibrate wide-baseline cameras, and align video streams, demonstrating robust performance in both real-time and deep learning frameworks.
• Applications span real-time stereo disparity estimation, distributed camera synchronization, diffusion-based stereo audio generation, and wireless loudspeaker sample-rate compensation.

Searching arXiv for papers titled or referring to “StereoSync” and closely related systems. StereoSync is a recurrent research name rather than a single canonical method. In the arXiv record, it denotes several technically distinct systems centered on synchronization and stereo structure: a realtime time synchronized event-based stereo method for moving event cameras (Zhu et al., 2018); a method for simultaneous synchronization and calibration for wide-baseline stereo event cameras (Xing et al., 2023); a software system for precisely time-synchronizing multiple distributed cameras over WiFi (Ansari et al., 2018); a deep learning-based pipeline for stereo camera multi-video synchronization (Boizard et al., 2023); a diffusion-based model for spatially-aware stereo audio generation from video (Marinoni et al., 7 Oct 2025); and a two-loudspeaker synchronization chain for stereo reproduction in the presence of sample rate offsets (Korse et al., 7 Jul 2025). This usage suggests that “StereoSync” functions as a label for systems in which temporal alignment is inseparable from stereo correspondence, stereo rendering, or stereo perception.

1. Name, scope, and major usages

The shared name spans event-based vision, conventional multi-camera capture, video synchronization, video-to-audio generation, and wireless stereo playback. In each case, synchronization is not treated as a purely auxiliary preprocessing step: it is integrated into disparity estimation, epipolar calibration, capture timing, multimodal conditioning, or sample-rate compensation.

Usage	Domain	Defining formulation
TSES (Zhu et al., 2018)	Event-based stereo	Time synchronized event disparity volumes with an Intersection-over-Union–style cost
StereoSync (Xing et al., 2023)	Wide-baseline stereo event cameras	Joint estimation of constant time offset $t_d$ and extrinsics $[R|t]$
Software system (Ansari et al., 2018)	Distributed smartphones	Leader/client clock synchronization plus phase alignment
Deep learning pipeline (Boizard et al., 2023)	Stereo video streams	Frame-pair matching network plus delay-estimation network
StereoSync (Marinoni et al., 7 Oct 2025)	Video-to-audio generation	Spatial conditioning from depth maps and bounding boxes in latent diffusion
StereoSync system (Korse et al., 7 Jul 2025)	Wireless loudspeakers	Spatial filtering, DWACD SRO estimation, and resampling compensation

A common misconception is to assume that StereoSync names a single algorithm. In the cited literature, it instead refers to multiple systems that share a synchronization-centric design philosophy while differing substantially in modality, objective, and mathematical machinery.

2. Time-synchronized event-based stereo

In "Realtime Time Synchronized Event-based Stereo," the problem is event stereo under camera motion, where motion blur arises because events at different times correspond to points that have moved in the image (Zhu et al., 2018). The setup uses two rectified event cameras, with events $e_i=(x_i,y_i,t_i,p_i)$ and known instantaneous rigid-body velocity of the left camera: linear velocity $\mathbf v=(v_x,v_y,v_z)^T$ and angular velocity $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$. Correspondence reduces to a 1D search over horizontal disparity $d$.

The central operation is a time-synchronization warp to a common reference time $t'$. For candidate disparity $d$, depth is

$Z(d)=\frac{f\,b}{d},$

and the induced image-plane velocity is

$\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$

Each event is then warped by

$[R|t]$0

At the correct disparity, events from the same physical 3D point align at $[R|t]$1 and no motion blur remains.

StereoSync represents the warped events with a pair of time synchronized event disparity volumes:

$[R|t]$2

$[R|t]$3

At the true disparity, corresponding features become sharp; at wrong disparities they remain blurred. The matching cost uses a local $[R|t]$4 window $[R|t]$5 and combines a union term, which penalizes blur, with an intersection term, which rewards matching polarity:

$[R|t]$6

$[R|t]$7

$[R|t]$8

Winner-takes-all disparity selection is

$[R|t]$9

Implementation is fully vectorized on GPU in TensorFlow, including motion-field computation, warping, volume construction via scatter_nd, convolution for window sums, and argmin. On an NVIDIA 960M laptop GPU, a $e_i=(x_i,y_i,t_i,p_i)$0 window and a 32-disparity sweep take approximately 40 ms per 15 000 events, or about $e_i=(x_i,y_i,t_i,p_i)$1s/event. Outlier rejection discards pixels with too few events or poor matches.

Evaluation on MVSEC indoor_flying1–3, with ground truth from LiDAR and disparities restricted to $e_i=(x_i,y_i,t_i,p_i)$2, showed TSES mean disparity error $e_i=(x_i,y_i,t_i,p_i)$3 px, mean depth error $e_i=(x_i,y_i,t_i,p_i)$4 m, and percentage of pixels with $e_i=(x_i,y_i,t_i,p_i)$5error$e_i=(x_i,y_i,t_i,p_i)$6 px equal to $e_i=(x_i,y_i,t_i,p_i)$7. CopNet yielded $e_i=(x_i,y_i,t_i,p_i)$8 px, $e_i=(x_i,y_i,t_i,p_i)$9 m, and $\mathbf v=(v_x,v_y,v_z)^T$0; frame-based block matching yielded $\mathbf v=(v_x,v_y,v_z)^T$1 px, $\mathbf v=(v_x,v_y,v_z)^T$2 m, and $\mathbf v=(v_x,v_y,v_z)^T$3; SGM was reported as significantly worse. The paper’s interpretation is explicit: at the correct disparity slice, the warp removes all relative motion, whereas wrong disparities increase $\mathbf v=(v_x,v_y,v_z)^T$4 and reduce $\mathbf v=(v_x,v_y,v_z)^T$5, producing a focus-versus-defocus effect that sharpens the cost peak.

3. Event-camera synchronization as epipolar optimization

A distinct StereoSync formulation treats synchronization itself as an unknown in stereo event geometry (Xing et al., 2023). The problem is defined for two event cameras $\mathbf v=(v_x,v_y,v_z)^T$6 and $\mathbf v=(v_x,v_y,v_z)^T$7 with known intrinsics and unknown constant time offset $\mathbf v=(v_x,v_y,v_z)^T$8 together with unknown rigid transform $\mathbf v=(v_x,v_y,v_z)^T$9. The temporal misalignment model is

$\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$0

and the synchronized epipolar constraint becomes

$\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$1

Here $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$2 is the fundamental matrix.

StereoSync converts this into an optimization over average point-to-epipolar-line distance. For time samples $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$3,

$\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$4

and the average cost is

$\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$5

where $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$6. With known extrinsics, the method performs a grid search over candidate offsets. With unknown extrinsics, it robustly fits $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$7 for each candidate offset using LMedS on tentative correspondences $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$8, selects $\boldsymbol\omega=(\omega_x,\omega_y,\omega_z)^T$9, and then decomposes the resulting essential matrix to recover $d$0 up to scale.

The correspondence source is also specific: a single dominant moving object in a mostly static scene. Preprocessing includes statistical outlier removal in $d$1 space, lens-distortion correction, temporal binning around $d$2, centroid extraction, and rejection of low-support or border cases. The resulting centroid trajectories $d$3 and $d$4 form the basis for synchronization and calibration.

Quantitatively, ESIM simulations at $d$5 resolution with injected offsets $d$6 ms and search range $d$7 ms reported $d$8 error as low as $d$9 ms and $t'$0 ms, depending on duration and baseline angle, with $t'$1 between $t'$2 and $t'$3 and translation-direction error between $t'$4 and $t'$5. In small-scale real experiments with DAVIS346 cameras and injected timestamp offsets, one reported case at $t'$6 ms yielded $t'$7 ms, $t'$8, and $t'$9; another at $d$0 ms yielded $d$1 ms, $d$2, and $d$3. An outdoor drone experiment with a 20 m baseline reported a reconstructed 3D trajectory that aligns with the RTK curve under ICP with sub-decimeter accuracy in $d$4.

This line of work is adjacent to, but not identical with, stereo event lifetime estimation for dynamic vision sensors (Hadviger et al., 2019). That work assumes hardware-synced DAVIS240 sensors and focuses on estimating event lifetimes from the surface of active events in order to build sharp gradient images for stereo matching. It reported nearly 50% faster runtime for the proposed single-shot method than for a decoupled baseline, with examples such as 32 ms versus 16 ms and 30 ms versus 15 ms. The juxtaposition is important: one direction estimates inter-camera time offset explicitly, whereas the other assumes synchronization and exploits it to improve disparity estimation.

4. Software synchronization of conventional video cameras

In conventional cameras, StereoSync appears in two markedly different software-only formulations: network-and-timestamp synchronization for distributed smartphones, and learned content-based synchronization for stereo video streams.

The distributed-camera system of "Wireless Software Synchronization of Multiple Distributed Cameras" uses two stages: clock synchronization by a leader/client four-timestamp handshake over WiFi, followed by stream phase alignment during continuous image capture (Ansari et al., 2018). For handshake timestamps $d$5, the round-trip time and clock offset are estimated as

$d$6

$d$7

The paper contrasts filtering strategies: a mean filter can suffer systematic bias on commodity WiFi, whereas the min filter follows NTP best practice. In 10 000-sample tests on Pixel 3 phones, min-filtered one-way asymmetry was 38 $d$8s versus 746 $d$9s for the mean. Phase alignment defines

$Z(d)=\frac{f\,b}{d},$0

and can be achieved either by repeated restart sampling or by frame injection. The reported full system achieved less than 250 microseconds on unmodified commodity hardware; after min-filter clock sync with $Z(d)=\frac{f\,b}{d},$1 and phase alignment to $Z(d)=\frac{f\,b}{d},$2s, 239 independent trials yielded worst-case total error 121 $Z(d)=\frac{f\,b}{d},$3s one-way, mean absolute total error 32 $Z(d)=\frac{f\,b}{d},$4s, and phase-only mean error 7 $Z(d)=\frac{f\,b}{d},$5s. The system was implemented as libsoftwaresync and demonstrated improved multi-view stereo depth estimation and dynamic-scene stitching.

The deep learning-based stereo camera multi-video synchronization work formulates synchronization as a two-stage pipeline: a frame-pair matching network computes a similarity score $Z(d)=\frac{f\,b}{d},$6, and a delay-estimation network predicts the integer temporal offset $Z(d)=\frac{f\,b}{d},$7 from the full matching matrix (Boizard et al., 2023). The optimization target is

$Z(d)=\frac{f\,b}{d},$8

with matching matrix

$Z(d)=\frac{f\,b}{d},$9

The paper compares CNNSiamese, TripletEuc, and TripletSim for frame matching, as well as a non-learned HeatMap delay estimator and a learned DenseDelay MLP. Inputs are grayscale $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$0 frames or dense optical-flow maps from Farneback. The best model, TripletEuc plus DenseDelay on optical-flow inputs, achieved average exact-match F1 of about 74.8% and average MAE of about 2.80 frames, with under 5 ms to compute one $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$1 matching matrix and less than 1 ms for the delay head on an NVIDIA RTX-2080 GPU.

Taken together, these two systems show that software synchronization can mean either clock-domain alignment with hardware timestamps and network messaging, or content-domain offset estimation from the video itself. A plausible implication is that “software-based synchronization” in stereo systems is not methodologically uniform: it ranges from NTP-like estimation and capture control to learned temporal inference.

5. Spatially-aware stereo audio generation from video

In "StereoSync: Spatially-Aware Stereo Audio Generation from Video," StereoSync designates a lightweight, diffusion-based framework for generating stereo audio that is both temporally synchronized and spatially aligned with an input video (Marinoni et al., 7 Oct 2025). The pipeline is explicitly modular: global geometry is extracted by RollingDepth as dense depth maps at 4 fps; object motion is extracted by MASA as bounding-box tracks at 30 fps; depth frames are encoded by EVAClip-ViT-G into 768-D embeddings and projected to 512-D; bounding-box coordinates are encoded by Stable Audio’s NumberConditioner; audio semantics come from a CLAP encoder; and the temporal envelope enters a ControlNet branch.

The latent diffusion core is based on Stable Audio Open’s U-Net in latent space at 44.1 kHz stereo. Only the projection layers and ControlNet weights are trained; all foundation model backbones remain frozen. The noisy latent attends to spatial and semantic context

$\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$2

with standard cross-attention

$\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$3

Temporal synchronization is driven by an RMS envelope extracted from ground-truth stereo audio:

$\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$4

followed by up-sampling to $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$5. ControlNet is trained with a $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$6-prediction loss,

$\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$7

The latent diffusion model operates on $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$8, preserving stereo channels, and the final decoder maps $\begin{pmatrix}\dot x_i(d)\ \dot y_i(d)\end{pmatrix} =\frac{1}{Z(d)} \begin{bmatrix}-1&0&x_i\0&-1&y_i\end{bmatrix}\mathbf v + \begin{bmatrix} x_i y_i & -(1+x_i^2)& y_i\ 1+y_i^2 & -x_i y_i & -x_i \end{bmatrix}\boldsymbol\omega.$9 to $[R|t]$00. No additional post-hoc mixing is required.

Training uses Walking The Maps, a dataset of 893 clips of game-engine footage from Hogwarts Legacy, Zelda, and Assassin’s Creed, with footstep audio isolated via AudioSep using the query “footstep sounds.” Hyperparameters include an NVIDIA RTX A6000, batch size 12, 3 000 training steps, AdamW with learning rate $[R|t]$01, and diffusion timesteps inherited from Stable Audio. Evaluation uses Fréchet Audio Distance on CLAP embeddings, Fréchet Audio-Visual Distance using I3D and VGGish, Spatial AV-Align defined as $[R|t]$02, and Envelope L1. Quantitatively, StereoSync reported AV-Align 0.78, FAD 0.230, FAVD 3.8301, and E-L1 0.047; the ablation without spatial conditioning reported 0.61, 0.256, 3.0682, and 0.062; Ground Truth AV-Align was 0.89. The paper states that the ablation confirms spatial cues raise AV-Align from 0.61 to 0.78, with minimal impact on FAD, FAVD, and E-L1.

Here the meaning of “stereo” shifts from binocular geometry to two-channel audio, yet the design pattern remains recognizably StereoSync: a synchronization objective is fused with a stereo output space and driven by structured side information rather than treated as an external alignment step.

6. Wireless stereo reproduction under sample-rate offsets

A further StereoSync usage concerns playback rather than capture or generation. In "Stereo Reproduction in the Presence of Sample Rate Offsets," the problem is clock skew between two wirelessly connected loudspeakers with independent clocks (Korse et al., 7 Jul 2025). If the second device runs at

$[R|t]$03

then, in a simple one-speaker listening-path model,

$[R|t]$04

The drift introduced by $[R|t]$05 degrades interaural time differences and interchannel coherence.

The proposed system first isolates each loudspeaker’s contribution at a microphone array using a Linearly Constrained Minimum Variance beamformer. In the STFT domain,

$[R|t]$06

and for speaker $[R|t]$07 the beamformer output is

$[R|t]$08

with

$[R|t]$09

Once $[R|t]$10 is obtained, the system estimates the sample-rate offset via Dynamic Weighted Average Coherence Drift. The method computes coherence,

$[R|t]$11

forms a phase-drift product over a temporal offset $[R|t]$12,

$[R|t]$13

smooths it in time, obtains a generalized cross-correlation by inverse DFT, estimates a coarse SRO

$[R|t]$14

and then refines it sub-sample.

Compensation is then performed either in the STFT domain by multiplying each frame by the inverse phase-drift factor or equivalently in the time domain by resampling:

$[R|t]$15

Objective evaluation in simulated $[R|t]$16 m rooms with $[R|t]$17 s and SRO pairs $[R|t]$18, $[R|t]$19, and $[R|t]$20 ppm showed that without compensation coherence drops rapidly and ITD drifts, whereas with perfect compensation both match the no-SRO reference. With the proposed oracle-RTF SpatialSync beamformer plus DWACD compensation, low- and mid-frequency cues are nearly fully restored, with only a small residual error at high frequencies. Subjective evaluation with a MUSHRA listening test using 11 listeners and 25 s excerpts reported that no compensation was clearly degraded at about 45–60 MUSHRA, oracle-RTF compensation recovered most of the score at about 80–90, and ground-truth compensation nearly reached the hidden reference at about 95.

This system broadens the StereoSync label further: synchronization is not only about timestamp consistency but also about compensating ongoing rate mismatch so that stereo perceptual cues remain stable over time.

7. Recurring principles, limitations, and interpretive synthesis

Across these works, StereoSync repeatedly combines an explicit synchronization variable with a stereo objective. In event-based stereo it is disparity-conditioned temporal warping and focus-versus-defocus matching (Zhu et al., 2018). In wide-baseline event-camera synchronization it is the constant offset $[R|t]$21 embedded directly into epipolar geometry (Xing et al., 2023). In distributed camera capture it is clock offset $[R|t]$22 and streaming phase $[R|t]$23 (Ansari et al., 2018). In deep-learning video synchronization it is the discrete offset $[R|t]$24 inferred from a learned matching matrix (Boizard et al., 2023). In video-to-audio generation it is temporal envelope guidance and spatial conditioning within a stereo latent diffusion model (Marinoni et al., 7 Oct 2025). In wireless loudspeaker playback it is sample-rate offset $[R|t]$25 and its compensation by spatial filtering and resampling (Korse et al., 7 Jul 2025).

The limitations are equally domain-specific. Event-camera synchronization by centroid trajectories assumes a single dominant moving object and leaves translation scale ambiguous (Xing et al., 2023). TSES assumes known instantaneous rigid-body velocity of the left camera and rectified, extrinsically calibrated sensors (Zhu et al., 2018). The WiFi smartphone system assumes reasonably stationary network latency, hardware timestamps in a common clock domain, and is constrained by Android hotspot limits (Ansari et al., 2018). The learned multi-video system estimates an integer frame offset rather than subframe timing and reports average MAE of about 2.80 frames (Boizard et al., 2023). The audio-generation model depends on pretrained frozen backbones and was evaluated on a curated dataset of game-engine walking clips (Marinoni et al., 7 Oct 2025). The loudspeaker system relies on spatial filtering quality and reports small residual high-frequency error (Korse et al., 7 Jul 2025).

A second misconception is that “stereo” has a fixed meaning across these papers. In the cited literature it refers variously to rectified binocular disparity, wide-baseline event-camera geometry, pairs of smartphone image streams, stereo video streams, stereo audio generation, and two-loudspeaker stereo reproduction. This suggests that the unifying concept behind StereoSync is not a common sensor modality or architecture, but a recurring research strategy: synchronization is elevated from a preprocessing nuisance to a first-class variable in stereo inference, capture, synthesis, or playback.