3 citations · 7 across the 15 of their papers we have counts for
17 papers · 1 filter
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…
Streaming Detection of Queried Event Start
Cristobal Eyzaguirre, Eric Tang, Shyamal Buch +3
Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this se…
ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation
Sergey Zakharov, Katherine Liu, Adrien Gaidon +1
The common trade-offs of state-of-the-art methods for multi-shape representation (a single model "packing" multiple objects) involve trading modeling accuracy against memory and st…
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields
Muhammad Zubair Irshad, Sergey Zakharov, Vitor Guizilini +3
Neural fields excel in computer vision and robotics due to their ability to understand the 3D visual world such as inferring semantics, geometry, and dynamics. Given the capabiliti…
Zero-Shot Multi-Object Scene Completion
Shun Iwase, Katherine Liu, Vitor Guizilini +4
We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Despite notable advancements in…
Understanding Video Transformers via Universal Concept Discovery
Matthew Kowal, Achal Dave, Rares Ambrus +3
This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely, we seek to explain the decision-making process of video tran…