activity
20172025
most citedSemantically-Guided Representation Learning for Self-Supervised Monocular Depth

47 citations · 90 across the 32 of their papers we have counts for

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41 papers · 1 filter

cs.CV2025

OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World

Katherine Liu, Sergey Zakharov, Dian Chen +4

We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first…

cs.CV2025

AllTracker: Efficient Dense Point Tracking at High Resolution

Adam W. Harley, Yang You, Xinglong Sun +11

We introduce AllTracker: a model that estimates long-range point tracks by way of estimating the flow field between a query frame and every other frame of a video. Unlike existing…

cs.CV2025

SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting

Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad +4

Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability…

cs.CV2025

Understanding Complexity in VideoQA via Visual Program Generation

Cristobal Eyzaguirre, Igor Vasiljevic, Achal Dave +5

We propose a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). Previous efforts in benchmark design have relied on human expertise to design…

cs.CV2025

GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity

Takuya Ikeda, Sergey Zakharov, Muhammad Zubair Irshad +8

We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often strugg…

cs.CV2025

Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion

Vitor Guizilini, Muhammad Zubair Irshad, Dian Chen +2

Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-vi…