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20242026
most citedHI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction

7 citations · 7 across the 14 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals

Xuyi Hu, Francesco Palandra, Shangzhe Wu +3

Recovering articulated 4D representations of animals from monocular videos remains challenging due to the large diversity of quadruped morphologies and lack of animal 4D supervisio…

cs.CV2026

Elastic Triangle Splatting

Tian Shi, Shenhan Qian, Daniel Cremers

While neural rendering methods such as 3D Gaussian Splatting achieve remarkable visual fidelity, traditional polygonal meshes remain the backbone of established graphics pipelines.…

cs.CV2026

OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos

Oussema Dhaouadi, Zuria Bauer, Johannes Michael Meier +3

Continuous 6-DoF pose estimation is essential for autonomous UAV operations. Yet, existing visual odometry and SLAM methods accumulate drift and yield only relative, up-to-scale tr…

cs.CV2026

DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds

Weirong Chen, Keisuke Tateno, Hidenobu Matsuki +3

We address 4D reconstruction from partial point cloud sequences, where depth-sensor observations are incomplete, unordered, and lack explicit temporal correspondences. This geometr…

cs.CV2026

BA-T: An Iterative Transformer for Two-View Bundle Adjustment

Ganlin Zhang, Weirong Chen, Daniel Cremers +1

Feed-forward models for 3D reconstruction have achieved strong performance using deep cross-view attention to exchange information across images. However, these approaches often de…

cs.CV2026

VisDom: Sparse Novel View Synthesis with Visible Domain Constraint

Mariia Gladkova, Tarun Yenamandra, Edmond Boyer +3

Sparse novel view synthesis (NVS) remains challenging due to the ambiguity of recovering 3D geometry from few input views. While NeRF- and Gaussian Splatting (GS)-based methods per…

cs.CV2026

EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation

Abhishek Saroha, Huajian Zeng, Xingxing Zuo +2

Understanding and predicting object motion from egocentric video is fundamental to embodied perception and interaction. However, generating physically consistent 6DoF trajectories…

cs.CV2026

NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction

Weirong Chen, Chuanxia Zheng, Ganlin Zhang +2

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 geomet…

cs.CV2026

Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

Shenhan Qian, Ganlin Zhang, Shangzhe Wu +1

Reconstructing and tracking dynamic 3D scenes is a fundamental challenge in computer vision. Existing methods typically decouple geometry from motion: static multi-view reconstruct…

cs.CV2025

ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association

Ganlin Zhang, Shenhan Qian, Xi Wang +1

We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At i…

cs.CV2025

ProBA: Probabilistic Bundle Adjustment with the Bhattacharyya Coefficient

Jason Chui, Hector Andrade-Loarca, Daniel Cremers

Classical Bundle Adjustment (BA) is fundamentally limited by its reliance on precise metric initialization and prior camera intrinsics. While modern dense matchers offer high-fidel…

cs.CV2025

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,…

cs.CV2024

DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair

Weihang Li, Weirong Chen, Shenhan Qian +3

Recent advances in 3D Gaussian Splatting have shown promising results. Existing methods typically assume static scenes and/or multiple images with prior poses. Dynamics, sparse vie…