197 citations · 349 across the 52 of their papers we have counts for
10 papers · 1 filter
ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes
Sergey Zakharov, Rares Ambrus, Katherine Liu +1
Compact and accurate representations of 3D shapes are central to many perception and robotics tasks. State-of-the-art learning-based methods can reconstruct single objects but scal…
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
Tara Sadjadpour, Jie Li, Rares Ambrus +1
Multi-object tracking is a cornerstone capability of any robotic system. The quality of tracking is largely dependent on the quality of the detector used. In many applications, suc…
Photo-realistic Neural Domain Randomization
Sergey Zakharov, Rares Ambrus, Vitor Guizilini +2
Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed…
Depth Field Networks for Generalizable Multi-view Scene Representation
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matchi…
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus +3
Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance…
SpOT: Spatiotemporal Modeling for 3D Object Tracking
Colton Stearns, Davis Rempe, Jie Li +5
3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current…
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Prafull Sharma, Ayush Tewari, Yilun Du +7
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable an…
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
Adam W. Harley, Zhaoyuan Fang, Jie Li +2
Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cam…
Multi-Frame Self-Supervised Depth with Transformers
Vitor Guizilini, Rares Ambrus, Dian Chen +2
Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearanc…
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels
Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus +1
Self-supervised monocular depth estimation enables robots to learn 3D perception from raw video streams. This scalable approach leverages projective geometry and ego-motion to lear…