Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

We present a new test-time optimization method for estimating dense and long-range motion
from a video sequence. Prior optical flow or particle video tracking algorithms typically …

Learning-based optical flow estimation has been dominated with the pipeline of cost volume
with convolutions for flow regression, which is inherently limited to local correlations and …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

We present a novel method for local image feature matching. Instead of performing image
feature detection, description, and matching sequentially, we propose to first establish pixel …

Generating robust and reliable correspondences across images is a fundamental task for a
diversity of applications. To capture context at both global and local granularity, we propose …

Optical flow is an indispensable building block for various important computer vision tasks,
including motion estimation, object tracking, and disparity measurement. In this work, we …

Image Matching is a core component of all best-performing algorithms and pipelines in 3D
vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to …

Local feature matching is a computationally intensive task at the subpixel level. While
detector-based methods coupled with feature descriptors struggle in low-texture scenes …

As computer vision algorithms increase in capability, their applications in clinical systems
will become more pervasive. These applications include: diagnostics, such as colonoscopy …