3 citations · 9 across the 15 of their papers we have counts for
19 papers
RayTun3R: Online Camera Adaptation in 3D Foundation Models
Daniil Sinitsyn, Nikita Araslanov, Daniel Cremers
Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, , and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply…
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…
Scene-Centric Unsupervised Video Panoptic Segmentation
Christoph Reich, Oliver Hahn, Nikita Araslanov +4
Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task se…
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…
Generative Shape Reconstruction with Geometry-Guided Langevin Dynamics
Linus Härenstam-Nielsen, Dmitrii Pozdeev, Thomas Dagès +2
Reconstructing complete 3D shapes from incomplete or noisy observations is a fundamentally ill-posed problem that requires balancing measurement consistency with shape plausibility…
FlowFeat: Pixel-Dense Embedding of Motion Profiles
Nikita Araslanov, Anna Sonnweber, Daniel Cremers
Dense and versatile image representations underpin the success of virtually all computer vision applications. However, state-of-the-art networks, such as transformers, produce low-…
Scene-Centric Unsupervised Panoptic Segmentation
Oliver Hahn, Christoph Reich, Nikita Araslanov +3
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In con…
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction
Weirong Chen, Ganlin Zhang, Felix Wimbauer +4
Traditional SLAM systems, which rely on bundle adjustment, struggle with highly dynamic scenes commonly found in casual videos. Such videos entangle the motion of dynamic elements,…
It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data
Dominik Schnaus, Nikita Araslanov, Daniel Cremers
The platonic representation hypothesis suggests that vision and language embeddings become more homogeneous as model and dataset sizes increase. In particular, pairwise distances w…
CARLA Drone: Monocular 3D Object Detection from a Different Perspective
Johannes Meier, Luca Scalerandi, Oussema Dhaouadi +3
Existing techniques for monocular 3D detection have a serious restriction. They tend to perform well only on a limited set of benchmarks, faring well either on ego-centric car view…
DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting
Linus Härenstam-Nielsen, Lu Sang, Abhishek Saroha +2
Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an appro…
Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball
Simon Weber, Barış Zöngür, Nikita Araslanov +1
Hierarchy is a natural representation of semantic taxonomies, including the ones routinely used in image segmentation. Indeed, recent work on semantic segmentation reports improved…
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer +1
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus with…
Semantic Self-adaptation: Enhancing Generalization with a Single Sample
Sherwin Bahmani, Oliver Hahn, Eduard Zamfir +3
The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation. Previous studies relied on the assumption of a static model, i. e., once…
Dense Unsupervised Learning for Video Segmentation
Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows to learn dense feature representations direct…
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
Nikita Araslanov, Stefan Roth
We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationall…
Single-Stage Semantic Segmentation from Image Labels
Nikita Araslanov, Stefan Roth
Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available…
Markov Decision Process for Video Generation
Vladyslav Yushchenko, Nikita Araslanov, Stefan Roth
We identify two pathological cases of temporal inconsistencies in video generation: video freezing and video looping. To better quantify the temporal diversity, we propose a class…
Actor-Critic Instance Segmentation
Nikita Araslanov, Constantin Rothkopf, Stefan Roth
Most approaches to visual scene analysis have emphasised parallel processing of the image elements. However, one area in which the sequential nature of vision is apparent, is that…