REVIEW 3 cited by
Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present an algorithm (SOFAS) to estimate the optical flow of events generated by a dynamic vision sensor (DVS). Where traditional cameras produce frames at a fixed rate, DVSs produce asynchronous events in response to intensity changes with a high temporal resolution. Our algorithm uses the fact that events are generated by edges in the scene to not only estimate the optical flow but also to simultaneously segment the image into objects which are travelling at the same velocity. This way it is able to avoid the aperture problem which affects other implementations such as Lucas-Kanade. Finally, we show that SOFAS produces more accurate results than traditional optic flow algorithms.
Forward citations
Cited by 3 Pith papers
-
Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow
A pipeline that segments independently moving objects and estimates egomotion from event-based normal flow and IMU rotation, without optical flow or depth, validated on EVIMO2v2.
-
Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow
A Mamba-based spatio-temporal state space network estimates event-camera optical flow with 32 GMACs and 1.11 EPE on DSEC, claiming large compute savings over prior methods.
-
EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation
EDCFlow combines temporally dense, multi-scale feature differences at high resolution with a low-resolution cost volume to achieve accurate, efficient event-based optical flow.
Discussion (0). Continue with ORCID to comment.