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Greatest papers with code

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

4 Nov 2020apple/ml-hypersim

To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.

INDOOR SCENE UNDERSTANDING MULTI-TASK LEARNING SCENE UNDERSTANDING

Joint 2D-3D-Semantic Data for Indoor Scene Understanding

3 Feb 2017alexsax/2D-3D-Semantics

We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2. 5D and 3D domains, with instance-level semantic and geometric annotations.

INDOOR SCENE UNDERSTANDING SCENE UNDERSTANDING

Zero-Shot Multi-View Indoor Localization via Graph Location Networks

6 Aug 2020coldmanck/zero-shot-indoor-localization-release

In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization.

INDOOR LOCALIZATION

Semantic Scene Completion via Integrating Instances and Scene in-the-Loop

8 Apr 2021yjcaimeow/SISNet

The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.

INDOOR SCENE UNDERSTANDING SCENE UNDERSTANDING

An Information-Theoretic Metric of Transferability for Task Transfer Learning

ICLR 2019 YaojieBao/An-Information-theoretic-Metric-of-Transferability

An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.

CLASSIFICATION INDOOR SCENE UNDERSTANDING SCENE UNDERSTANDING TRANSFER LEARNING

3D Semantic Segmentation of Modular Furniture using rjMCMC

WACV 2017 2017 czarmanu/3D-semantic-segmentation-of-modular-furniture

In our approach we jointly estimate the number of functional units, their spatial structure, and their corresponding labels by using reversible jump MCMC (rjMCMC), a method well suited for optimization on spaces of varying dimensions (the number of structural elements).

FURNITURE SEGMENTATION INDOOR SCENE UNDERSTANDING ROBOTIC GRASPING SCENE UNDERSTANDING