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Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
TLDR
This paper presents a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks, and takes advantage of the availability of professional interior designs to automatically extract 3D structures from them.
Single-Image Piece-Wise Planar 3D Reconstruction via Associative Embedding
TLDR
A novel two-stage method based on associative embedding, inspired by its recent success in instance segmentation, that is able to detect an arbitrary number of planes and facilitate many real-time applications such as visual SLAM and human-robot interaction.
PPGNet: Learning Point-Pair Graph for Line Segment Detection
TLDR
This paper proposes to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods and introduces the PPGNet, a convolutional neural network that directly infers a graph from an image.
Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization
TLDR
A regression guided detection network (RDNet) is proposed for RGB-D crowd counting and a depth-aware anchor is designed for better initialization of anchor sizes in detection framework to improve the robustness of detection-based approaches for small/tiny heads.
Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery
TLDR
A novel learning-based depth estimation framework that leverages the geometric structure of a scene to conduct depth estimation and demonstrates that the method can be applied to counterfactual depth.
Cascaded ConvLSTMs Using Semantically-Coherent Data Synthesis for Video Object Segmentation
TLDR
This paper uses a more effective and efficient cascade module to refine the model predictions and proposes a semantically-coherent data synthesis strategy to augment training sequences without any efforts.
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
TLDR
The frame selection problem in the interactive VOS is formulated as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework, making the interactive setting more practical in the wild.
Layout-Guided Novel View Synthesis from a Single Indoor Panorama
TLDR
This paper makes the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration and uses Convolutional Neural Networks to extract the deep features and estimate the depth map from the source-view image.
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
TLDR
MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks and empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets.
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
TLDR
This paper employs Convolutional Neural Networks to detect planes and vertical lines between adjacent walls, and optimize the 3D plane parameters to reconstruct a geometrically consistent room layout between planes and lines.