2.3k citations · 4.7k across the 84 of their papers we have counts for
11 papers · 1 filter
ClothAtlas: Joint Garment Identification and State Estimation under Self-Occlusion
Jens Lundell, Alberta Longhini, Lei Shu +6
Garment manipulation requires inferring both garment identity and complete configuration from partial, self-occluded observations. This creates a chicken-and-egg problem: completin…
ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
Tara Sadjadpour, Rares Ambrus, Jeannette Bohg
3D multi-object tracking (MOT) is essential for an autonomous mobile agent to safely navigate a scene. In order to maximize the perception capabilities of the autonomous agent, we…
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects
Ruohan Gao, Yiming Dou, Hao Li +5
We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulatio…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
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…
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking
JunYoung Gwak, Silvio Savarese, Jeannette Bohg
Recent research in multi-task learning reveals the benefit of solving related problems in a single neural network. 3D object detection and multi-object tracking (MOT) are two heavi…
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer
Ruohan Gao, Zilin Si, Yen-Yu Chang +5
Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rathe…
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Hsu-kuang Chiu, Jie Li, Rares Ambrus +1
Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm wher…
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Hsu-kuang Chiu, Antonio Prioletti, Jie Li +1
3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we p…
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Xingyu Liu, Mengyuan Yan, Jeannette Bohg
Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called for learning repre…
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images
Mia Kokic, Danica Kragic, Jeannette Bohg
We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Ne…