activity
20242026
most citedLightweight and Accurate Multi-View Stereo with Confidence-Aware Diffusion Model

11 citations · 11 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV2026

LSA: Localized Semantic Alignment for Enhancing Temporal Consistency in Traffic Video Generation

Mirlan Karimov, Teodora Spasojevic, Markus Braun +3

Controllable video generation has emerged as a versatile tool for autonomous driving, enabling realistic synthesis of traffic scenarios. However, existing methods depend on control…

cs.CV2026

ImLoc: Revisiting Visual Localization with Image-based Representation

Xudong Jiang, Fangjinhua Wang, Silvano Galliani +2

Existing visual localization methods are typically either 2D image-based, which are easy to build and maintain but limited in effective geometric reasoning, or 3D structure-based,…

cs.CV2025

From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs

Mingrui Wu, Zhaozhi Wang, Fangjinhua Wang +3

While Multimodal Large Language Models (MLLMs) have achieved impressive performance on semantic tasks, their spatial intelligence--crucial for robust and grounded AI systems--remai…

cs.CV202511 cited

Lightweight and Accurate Multi-View Stereo with Confidence-Aware Diffusion Model

Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong +1

To reconstruct the 3D geometry from calibrated images, learning-based multi-view stereo (MVS) methods typically perform multi-view depth estimation and then fuse depth maps into a…

cs.CV2025

EgoM2P: Egocentric Multimodal Multitask Pretraining

Gen Li, Yutong Chen, Yiqian Wu +3

Understanding multimodal signals in egocentric vision, such as RGB video, depth, camera poses, and gaze, is essential for applications in augmented reality, robotics, and human-com…

cs.RO2025

MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos

Alexey Gavryushin, Xi Wang, Robert J. S. Malate +5

Large-scale egocentric video datasets capture diverse human activities across a wide range of scenarios, offering rich and detailed insights into how humans interact with objects,…