The image depicts a large poster for an N-LINESOLVER for linear and motion tracking. The poster is positioned on a flat surface, and a group of people are standing around it, likely discussing or analyzing the content. The poster is decorated with multiple diagrams, all of which are likely related to linear and motion tracking. The people in the image are all dressed in formal attire, and there is a sense of excitement and enthusiasm in the air. Overall, the image is a visual representation of a key scientific area, and the people in the image are likely researchers or students studying this area. Text transcribed from the image: 上海科技大学 ShanghaiTech University University of Zurich Motivation Highli An N-Point Linear Solver for Line and Motion Esti Use events generated by a 3D line and gyro. readings from IMU to recover partial linear velocity and line parameters with a fast and robust linear solver. Applying a novel velocity averaging scheme, we fuse these partial observations to obtain full linear camera velocity. Contributions 1. A linear solver for minimal and overdetermined (N>=5 events) systems, that is 600x faster than polynomial solvers. 2. A 3 DoF angle-axis-based line parametrization that improves the numerical stability of existing solvers. 3. A full characterization of degeneracies and solutions of the solver, and manifolds spanned by the events. 4. A geometry-inspired velocity averaging scheme that is simpler and faster than existing method. What is an event camera? Ling Gao*, Daniel Gehrig*, Hang Su, Davide Scaramu Methodology: Events from temporal slice Linear Solver Stack linear incidence relations, one for each event Solve for line and velocity unknowns using SVD Reconstruct the unknowns using simple vector algebra Incidence Relationship Extract manifold *indicates equal contribution Average partial velocity results linear velocity projection Velocity Averaging Scheme 3D line params. L-eee •Stack linear velocity constraint, one for each line • Solve for full velocity using SVD P rotated event bearing vector camera C frame e e 3D line Partial Velocity Measurements m R Standard camera output 0000000 stock - camera linear velocity y origin unknowns simplify Unobservable due to ef ((Rut))+(-e) = 0 => f(ue-use) += 0 measurements aperture problem line index RR =0 last column of V from SVD/A) => e = v = + + geometric constraint => v (e event camera output no motion, no events! reconstruct unknowns sures a stream of asynchronous brightness changes ("events") antages: high temporal resolution, reduced motion blur, low er consumption, high pixel bandwidth, high dynamic range tiple Solutions roposed solver returns up to 4 different solutions symmetry corresponds to flipping along the z-axis The second corresponds to flipping the line direction • Disambiguate by checking the line is "in front of the camera" AER e=246 u = 46 21:3×24:6 multiple solutions collect variables -> Characterizing Event Manifolds flipping flipping line direction flipping line direction flipping z-axis -Bum A de-rotate events with rotate into canonical frame IMU angular rates All event manifolds can be mapped into a canonical representation in frame R In this frame, the manifold is only parametrized by u, u u controls the curvature, while u controls the slope 0 = [213] - [ue - ue] multiple solutions Solution R [ee] 0 = (log R₂) u = [0 2 น.] stack last column of V unen-une Soluti from SVD(A) v=0 v = + AERNX3 Quantitative Results Our linear solver and velocity averaging sch can be extended to an arbitrary number of ev Pixel Noise (0.5 px) Time. Jitter (0.5 ms) Gyro. Noise (5.0°/s) 6 7 8 9 10 20 30 40 Number of used events 50 100 1000 As few as 10 events are sufficient to substantially reduce the error from noisy measurements. Direction Emm • As e