Search Results for author: Manolis Savva

Found 58 papers, 30 papers with code

Linking WordNet to 3D Shapes

no code implementations GWC 2018 Angel X Chang, Rishi Mago, Pranav Krishna, Manolis Savva, Christiane Fellbaum

We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes.

Survey on Modeling of Articulated Objects

no code implementations22 Mar 2024 Jiayi Liu, Manolis Savva, Ali Mahdavi-Amiri

3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics.


Text-to-3D Shape Generation

no code implementations20 Mar 2024 Han-Hung Lee, Manolis Savva, Angel X. Chang

Recent years have seen an explosion of work and interest in text-to-3D shape generation.

3D Shape Generation Representation Learning +1

R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding

no code implementations18 Mar 2024 Qirui Wu, Sonia Raychaudhuri, Daniel Ritchie, Manolis Savva, Angel X Chang

We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D scenes mirroring the real-world scene arrangements from Matterport3D panoramas.

Object Scene Understanding

Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects

no code implementations31 Dec 2023 Qirui Wu, Daniel Ritchie, Manolis Savva, Angel X. Chang

Single-view 3D shape retrieval is a challenging task that is increasingly important with the growth of available 3D data.

3D Shape Retrieval Retrieval

CAGE: Controllable Articulation GEneration

no code implementations15 Dec 2023 Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, Manolis Savva

We address the challenge of generating 3D articulated objects in a controllable fashion.

Denoising Object

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

1 code implementation30 Nov 2023 Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei HUANG, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.

Video Understanding

LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning

1 code implementation19 Oct 2023 Pedram Agand, Mohammad Mahdavian, Manolis Savva, Mo Chen

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques and navigational control methods for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents.

Autonomous Driving Imitation Learning +3

PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects

1 code implementation ICCV 2023 Jiayi Liu, Ali Mahdavi-Amiri, Manolis Savva

Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3. 94 (45. 2%) for objects and 26. 79 (84. 5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories.

Motion Estimation Object

Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation

no code implementations20 Jun 2023 Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra, Alexander Clegg, Eric Undersander, Angel X. Chang, Manolis Savva

Surprisingly, we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10, 000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments.

Navigate Zero-shot Generalization

Evaluating 3D Shape Analysis Methods for Robustness to Rotation Invariance

no code implementations29 May 2023 Supriya Gadi Patil, Angel X. Chang, Manolis Savva

Our study, on a synthetic dataset of 3D scenes where objects instances occur in different orientations, reveals that deep learning-based rotation invariant methods are effective for relatively easy settings with easy-to-distinguish pairs.

MOPA: Modular Object Navigation with PointGoal Agents

no code implementations7 Apr 2023 Sonia Raychaudhuri, Tommaso Campari, Unnat Jain, Manolis Savva, Angel X. Chang

We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI.

Navigate Object +3

OPDMulti: Openable Part Detection for Multiple Objects

1 code implementation24 Mar 2023 Xiaohao Sun, Hanxiao Jiang, Manolis Savva, Angel Xuan Chang

We then address this more challenging scenario with OPDFormer: a part-aware transformer architecture.


Emergence of Maps in the Memories of Blind Navigation Agents

no code implementations30 Jan 2023 Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra

A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial.

Inductive Bias PointGoal Navigation

OPD: Single-view 3D Openable Part Detection

1 code implementation30 Mar 2022 Hanxiao Jiang, Yongsen Mao, Manolis Savva, Angel X. Chang

The input is a single image of an object, and as output we detect what parts of the object can open, and the motion parameters describing the articulation of each openable part.

Object OPD: Single-view 3D Openable Part Detection

Learning Body-Aware 3D Shape Generative Models

no code implementations13 Dec 2021 Bryce Blinn, Alexander Ding, R. Kenny Jones, Manolis Savva, Srinath Sridhar, Daniel Ritchie

The body-shape-conditioned models produce chairs which will be comfortable for a person with the given body shape; the pose-conditioned models produce chairs which accommodate the given sitting pose.

Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms

no code implementations10 Dec 2021 Kai Wang, Xianghao Xu, Leon Lei, Selena Ling, Natalie Lindsay, Angel X. Chang, Manolis Savva, Daniel Ritchie

We then discuss different strategies for solving the problem, and design two representative pipelines: one uses available 2D floor plans to guide selection and deformation of 3D rooms; the other learns to retrieve a set of compatible 3D rooms and combine them into novel layouts.

3D Reconstruction Autonomous Navigation +2

D3D-HOI: Dynamic 3D Human-Object Interactions from Videos

2 code implementations19 Aug 2021 Xiang Xu, Hanbyul Joo, Greg Mori, Manolis Savva

We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos.

Human-Object Interaction Detection Object

Mirror3D: Depth Refinement for Mirror Surfaces

1 code implementation CVPR 2021 Jiaqi Tan, Weijie Lin, Angel X. Chang, Manolis Savva

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors.

3D Reconstruction Depth Estimation

Plan2Scene: Converting Floorplans to 3D Scenes

1 code implementation CVPR 2021 Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa, Angel X. Chang, Manolis Savva

We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene.


Large Batch Simulation for Deep Reinforcement Learning

1 code implementation ICLR 2021 Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian

We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.

PointGoal Navigation reinforcement-learning +1

LayoutGMN: Neural Graph Matching for Structural Layout Similarity

1 code implementation CVPR 2021 Akshay Gadi Patil, Manyi Li, Matthew Fisher, Manolis Savva, Hao Zhang

In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.

Graph Matching Metric Learning +1

MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation

no code implementations NeurIPS 2020 Saim Wani, Shivansh Patel, Unnat Jain, Angel X. Chang, Manolis Savva

We propose the multiON task, which requires navigation to an episode-specific sequence of objects in a realistic environment.

Benchmarking Object

ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

3 code implementations23 Jun 2020 Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets, Roozbeh Mottaghi, Manolis Savva, Alexander Toshev, Erik Wijmans

In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e. g., find a chair, by navigating to it.


DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

8 code implementations ICLR 2020 Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra

We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.

Autonomous Navigation Navigate +2

Relational Graph Learning for Crowd Navigation

1 code implementation28 Sep 2019 Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva

We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.

Graph Learning Reinforcement Learning (RL)

Scan2CAD: Learning CAD Model Alignment in RGB-D Scans

2 code implementations CVPR 2019 Armen Avetisyan, Manuel Dahnert, Angela Dai, Manolis Savva, Angel X. Chang, Matthias Nießner

For a 3D reconstruction of an indoor scene, our method takes as input a set of CAD models, and predicts a 9DoF pose that aligns each model to the underlying scan geometry.

3D Reconstruction

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

no code implementations CVPR 2018 Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, Thomas Funkhouser

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 view of an indoor scene when given only a partial observation ( <=50%) in the form of an RGB-D image.

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

no code implementations12 Dec 2017 Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, Thomas Funkhouser

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 view of an indoor scene when given only a partial observation (<= 50%) in the form of an RGB-D image.

MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

2 code implementations11 Dec 2017 Manolis Savva, Angel X. Chang, Alexey Dosovitskiy, Thomas Funkhouser, Vladlen Koltun

We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments.

Navigate reinforcement-learning +1

Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes

no code implementations7 Apr 2017 Kyle Genova, Manolis Savva, Angel X. Chang, Thomas Funkhouser

We provide a search algorithm that generates a sampling of likely candidate views according to the example distribution, and a set selection algorithm that chooses a subset of the candidates that jointly cover the example distribution.

Segmentation Semantic Segmentation

SceneSuggest: Context-driven 3D Scene Design

no code implementations28 Feb 2017 Manolis Savva, Angel X. Chang, Maneesh Agrawala

We present SceneSuggest: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement.

Graphics Human-Computer Interaction

SceneSeer: 3D Scene Design with Natural Language

no code implementations28 Feb 2017 Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning

We present SceneSeer: an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language.

Scene Generation Text to 3D

Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

no code implementations CVPR 2017 Yinda Zhang, Shuran Song, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin, Thomas Funkhouser

One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection.

Boundary Detection Edge Detection +4

Semantic Scene Completion from a Single Depth Image

3 code implementations CVPR 2017 Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation.

3D Semantic Scene Completion

Text to 3D Scene Generation with Rich Lexical Grounding

no code implementations IJCNLP 2015 Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, Christopher D. Manning

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.

Scene Generation Text to 3D

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