5 citations · 6 across the 10 of their papers we have counts for
13 papers
Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles
NVIDIA, :, Sérgio Agostinho +25
We introduce Foundation4D, a unified feed-forward model for geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi-camera driving video. A sh…
Déjà View: Looping Transformers for Multi-View 3D Reconstruction
Alessandro Burzio, Tobias Fischer, Sven Elflein +9
Recent feed-forward 3D reconstruction transformers have scaled to over a billion parameters, following the broader trend of increasing model capacity in computer vision. Yet emergi…
A Guide to Structureless Visual Localization
Vojtech Panek, Qunjie Zhou, Yaqing Ding +4
Visual localization algorithms, i.e., methods that estimate the camera pose of a query image in a known scene, are core components of many applications, including self-driving cars…
LongPerceptualThoughts: Distilling System-2 Reasoning for System-1 Perception
Yuan-Hong Liao, Sven Elflein, Liu He +4
Recent reasoning models through test-time scaling have demonstrated that long chain-of-thoughts can unlock substantial performance boosts in hard reasoning tasks such as math and c…
Towards Learning to Complete Anything in Lidar
Ayca Takmaz, Cristiano Saltori, Neehar Peri +4
We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contem…
Zero-Shot 4D Lidar Panoptic Segmentation
Yushan Zhang, Aljoša Ošep, Laura Leal-Taixé +1
Zero-shot 4D segmentation and recognition of arbitrary objects in Lidar is crucial for embodied navigation, with applications ranging from streaming perception to semantic mapping…
Towards a Unified Copernicus Foundation Model for Earth Vision
Yi Wang, Zhitong Xiong, Chenying Liu +8
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…
MATCHA:Towards Matching Anything
Fei Xue, Sven Elflein, Laura Leal-Taixé +1
Establishing correspondences across images is a fundamental challenge in computer vision, underpinning tasks like Structure-from-Motion, image editing, and point tracking. Traditio…
Light3R-SfM: Towards Feed-forward Structure-from-Motion
Sven Elflein, Qunjie Zhou, Sérgio Agostinho +1
We present Light3R-SfM, a feed-forward, end-to-end learnable framework for efficient large-scale Structure-from-Motion (SfM) from unconstrained image collections. Unlike existing S…
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
Yizhou Wang, Tim Meinhardt, Orcun Cetintas +6
Object perception from multi-view cameras is crucial for intelligent systems, particularly in indoor environments, e.g., warehouses, retail stores, and hospitals. Most traditional…
DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction
Jenny Seidenschwarz, Qunjie Zhou, Bardienus Duisterhof +2
Reconstructing scenes and tracking motion are two sides of the same coin. Tracking points allow for geometric reconstruction [14], while geometric reconstruction of (dynamic) scene…
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Linyan Yang, Lukas Hoyer, Mark Weber +6
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that c…
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Orcun Cetintas, Tim Meinhardt, Guillem Brasó +1
Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-sca…