Search Results for author: Manolis Savva

Found 40 papers, 22 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.

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 +1

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

1 code implementation19 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

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.

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

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

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.

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

6 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 PointGoal Navigation +1

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 Convolutional Network Graph Learning

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

On Evaluation of Embodied Navigation Agents

9 code implementations18 Jul 2018 Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence.

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.

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.

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

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.

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

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