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20172022
most citedMatterport3D: Learning from RGB-D Data in Indoor Environments

337 citations · 565 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CV2022173 cited

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Andy Zeng, Maria Attarian, Brian Ichter +10

Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barel…

cs.CV2019

Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations

Shuran Song, Andy Zeng, Johnny Lee +1

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to…

cs.CV201920 cited

ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

Shreeyak S. Sajjan, Matthew Moore, Mike Pan +4

Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate de…

cs.CV201712 cited

Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View

Shuran Song, Andy Zeng, Angel X. Chang +3

We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic vi…

cs.CV2017337 cited

Matterport3D: Learning from RGB-D Data in Indoor Environments

Angel Chang, Angela Dai, Thomas Funkhouser +6

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a re…