8 accepted papers
We address the problem of 4D reconstruction from partial point cloud sequences, where observations from depth sensors are incomplete, unordered, and lack explicit point correspondence over time. Recovering coherent 4D geometry in this geometry-only setting is challenging due to missing observations …
We present NOVA3R, an effective approach for non-pixel-aligned 3D reconstruction from a set of unposed images, in a feed-forward manner. Unlike pixel-aligned methods that tie geometry to per-ray predictions, our formulation learns a global, view-agnostic scene representation that decouples reconstru…
Estimating camera motion and intrinsics from casual videos is a core challenge in computer vision. Traditional bundle-adjustment based methods, such as SfM and SLAM, struggle to perform reliably on arbitrary data. Although specialized SfM approaches have been developed for handling dynamic scenes, t…
Traditional SLAM systems, which rely on bundle adjustment, struggle with the highly dynamic scenes commonly found in casual videos. Such videos entangle the motion of dynamic elements, undermining the assumption of static environments required by traditional systems. Existing techniques either filte…
Visual odometry estimates the motion of a moving camera based on visual input. Existing methods mostly focusing on two-view point tracking often ignore the rich temporal context in the image sequence thereby overlooking the global motion patterns and providing no assessment of the full trajectory re…
As a promising fashion for visual localization, scene coordinate regression (SCR) has seen tremendous progress in the past decade. Most recent methods usually adopt neural networks to learn the mapping from image pixels to 3D scene coordinates, which requires a vast amount of annotated training data…
This work introducesan effective and practical solution to the dense two-view structure from motion (S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i>M) problem. One vital question addressed is how to mindfully use per-pixel optical flow corresp
This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and adopting search queries directly as their web labels. Although WSL benefits from fast and low-cost data expansion, noisy web labels prevent models from reliable predictions. To m…