6 papers
Segmenting, Fast and Slow: Real-Time Open-Vocabulary Video Instance Segmentation with Dual-Path Processing
Luca Barsellotti, Martin Sundermeyer, Mattia Segu +5
Object-centric models inspired by DETR have become the dominant paradigm for open-vocabulary video instance segmentation (OV-VIS). While recent efforts have reduced the computation…
Emergence of a Shared Canonical Object Frame from In-the-Wild Videos
Tom Fischer, Martin Sundermeyer, Adam Kortylewski +1
Comparing object orientations and positions across different instances requires their poses to be expressed in a shared canonical frame. Establishing such frames has traditionally…
XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity
Junwen Huang, Jiaqi Hu, Peter KT Yu +3
While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial envi…
Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki +2
One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack representations that effectiv…
TAPNext++: What's Next for Tracking Any Point (TAP)?
Sebastian Jung, Artem Zholus, Martin Sundermeyer +6
Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes…
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation
Van Nguyen Nguyen, Stephen Tyree, Andrew Guo +16
We present the evaluation methodology, datasets and results of the BOP Challenge 2024, the 6th in a series of public competitions organized to capture the state of the art in 6D ob…