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3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics.
Color indexing
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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Willmott, C. J.; and Matsuura, K. 2005 · 2005
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AUC: a misleading measure of the performance of predictive distribution models
Lobo, J. M.; Jiménez-Valverde, A.; and Real, R. 2008 · 2008
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Hico: A benchmark for recognizing human-object interactions in images
Chao, Y.-W.; Wang, Z.; He, Y.; Wang, J.; and Deng, J. 2015 · 2015
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Learning affordance landscapes for interaction exploration in 3d environments
Nagarajan, T.; and Grauman, K. 2020 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Optimizing intersection-over-union in deep neural networks for image segmentation
Rahman, M. A.; and Wang, Y. 2016 · 2016
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A multi-scale cnn for affordance segmentation in rgb images
Roy, A.; and Todorovic, S. 2016 · 2016
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R.; Yi, L.; Su, H.; and Guibas, L. J. 2017 · 2017
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Learning to act properly: Predicting and explaining affordances from images
Chuang, C.-Y.; Li, J.; Torralba, A.; and Fidler, S. 2018 · 2018
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Affordancenet: An end-to-end deep learning approach for object affordance detection
Do, T.-T.; Nguyen, A.; and Reid, I. 2018 · 2018
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Demo2vec: Reasoning object affordances from online videos
Fang, K.; Wu, T.-L.; Yang, D.; Savarese, S.; and Lim, J. J. 2018 · 2018
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Pointfusion: Deep sensor fusion for 3d bounding box estimation
Xu, D.; Anguelov, D.; and Jain, A. 2018 · 2018
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Multi-view pointnet for 3d scene understanding
Jaritz, M.; Gu, J.; and Su, H. 2019 · 2019
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Object affordance based multimodal fusion for natural human-robot interaction
Mi, J.; Tang, S.; Deng, Z.; Goerner, M.; and Zhang, J. 2019 · 2019
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PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding
Mo, K.; Zhu, S.; Chang, A. X.; Yi, L.; Tripathi, S.; Guibas, L. J.; and Su, H. 2019 · 2019
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Grounded human-object interaction hotspots from video
Nagarajan, T.; Feichtenhofer, C.; and Grauman, K. 2019 · 2019
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Fuseseg: Lidar point cloud segmentation fusing multi-modal data
Krispel, G.; Opitz, M.; Waltner, G.; Possegger, H.; and Bischof, H. 2020 · 2020
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Intention-related natural language grounding via object affordance detection and intention semantic extraction
Mi, J.; Liang, H.; Katsakis, N.; Tang, S.; Li, Q.; Zhang, C.; and Zhang, J. 2020 · 2020
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A deep learning approach to object affordance segmentation
Thermos, S.; Daras, P.; and Potamianos, G. 2020 · 2020
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Pointpainting: Sequential fusion for 3d object detection
Vora, S.; Lang, A. H.; Helou, B.; and Beijbom, O. 2020 · 2020
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Object affordance detection with relationship-aware network
Zhao, X.; Cao, Y.; and Kang, Y. 2020 · 2020
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3d affordancenet: A benchmark for visual object affordance understanding
O2O-Afford: Annotation-free large-scale object-object affordance learning
Mo, K.; Qin, Y.; Xiang, F.; Su, H.; and Guibas, L. 2022 · 2022
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Affordance grounding from demonstration video to target image
Chen, J.; Gao, D.; Lin, K. Q.; and Shou, M. Z. 2023 · 2023
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Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Cheng, K.; Wu, R.; Shen, Y.; Ning, C.; Zhan, G.; and Dong, H. 2023 · 2023
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Graspnerf: Multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf
Dai, Q.; Zhu, Y.; Geng, Y.; Ruan, C.; Zhang, J.; and Wang, H. 2023 · 2023
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Palm-e: An embodied multimodal language model
Driess, D.; Xia, F.; Sajjadi, M. S.; Lynch, C.; Chowdhery, A.; Ichter, B.; Wahid, A.; Tompson, J.; Vuong, Q.; Yu, T.; et al. 2023 · 2023
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Deng, S.; Xu, X.; Wu, C.; Chen, K.; and Jia, K. 2021 · 2021
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Luo, H.; Zhai, W.; Zhang, J.; Cao, Y.; and Tao, D. 2021 · 2021
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Where2act: From pixels to actions for articulated 3d objects
Mo, K.; Guibas, L. J.; Mukadam, M.; Gupta, A.; and Tulsiani, S. 2021 · 2021
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Mbdf-net: Multi-branch deep fusion network for 3d object detection
Tan, X.; Chen, X.; Zhang, G.; Ding, J.; and Lan, X. 2021 · 2021
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Vat-mart: Learning visual action trajectory proposals for manipulating 3d articulated objects
Wu, R.; Zhao, Y.; Mo, K.; Guo, Z.; Wang, Y.; Wu, T.; Fan, Q.; Chen, X.; Guibas, L.; and Dong, H. 2021 · 2021
Cited alongside, same era.
Similarity-Aware Fusion Network for 3D Semantic Segmentation
Zhao, L.; Lu, J.; and Zhou, J. 2021 · 2021
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Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation
Zhao, L.; Zhou, H.; Zhu, X.; Song, X.; Li, H.; and Tao, W. 2021 · 2021
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Instruct2act: Mapping multi-modality instructions to robotic actions with large language model
Huang, S.; Jiang, Z.; Dong, H.; Qiao, Y.; Gao, P.; and Li, H. 2023 · 2023
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G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory
Li, H.; Cao, M.; Cheng, X.; Li, Y.; Zhu, Z.; and Zou, Y. 2023 · 2023
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Multi-label affordance mapping from egocentric vision
Mur-Labadia, L.; Guerrero, J. J.; and Martinez-Cantin, R. 2023 · 2023
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Tidybot: Personalized robot assistance with large language models
Wu, J.; Antonova, R.; Kan, A.; Lepert, M.; Zeng, A.; Song, S.; Bohg, J.; Rusinkiewicz, S.; and Funkhouser, T. 2023 · 2023
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WorldAfford: Affordance Grounding based on Natural Language Instructions
Chen, C.; Cong, Y.; and Kan, Z. 2024 · 2024
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Scenefun3d: Fine-grained functionality and affordance understanding in 3d scenes
Delitzas, A.; Takmaz, A.; Tombari, F.; Sumner, R.; Pollefeys, M.; and Engelmann, F. 2024 · 2024
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A3VLM: Actionable Articulation-Aware Vision Language Model
Huang, S.; Chang, H.; Liu, Y.; Zhu, Y.; Dong, H.; Gao, P.; Boularias, A.; and Li, H. 2024 · 2024
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Far3d: Expanding the horizon for surround-view 3d object detection
Jiang, X.; Li, S.; Liu, Y.; Wang, S.; Jia, F.; Wang, T.; Han, L.; and Zhang, X. 2024 · 2024
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Instance-aware multi-camera 3d object detection with structural priors mining and self-boosting learning
Jiao, Y.; Jie, Z.; Chen, S.; Cheng, L.; Chen, J.; Ma, L.; and Jiang, Y.-G. 2024 · 2024
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Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects
Ning, C.; Wu, R.; Lu, H.; Mo, K.; and Dong, H. 2024 · 2024
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LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Rendering and Control
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Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving
Wang, Y.; He, J.; Fan, L.; Li, H.; Chen, Y.; and Zhang, Z. 2024 · 2024
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Regulating intermediate 3d features for vision-centric autonomous driving
Xu, J.; Peng, L.; Cheng, H.; Xia, L.; Zhou, Q.; Deng, D.; Qian, W.; Wang, W.; and Cai, D. 2024 · 2024
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Text-driven Affordance Learning from Egocentric Vision
Yoshida, T.; Kurita, S.; Nishimura, T.; and Mori, S. 2024 · 2024
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