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Found 24 papers, 11 papers with code
An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems
Our results indicated that users left significantly more feedback on items chosen for exploration with our interface.
Neural 3D Clothes Retargeting from a Single Image
In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RGB image.
Online Adaptation for Consistent Mesh Reconstruction in the Wild
This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild.
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics.
Bi3D: Stereo Depth Estimation via Binary Classifications
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera
Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents.
Two-shot Spatially-varying BRDF and Shape Estimation
Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
To the best of our knowledge, we are the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints.
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
In order to predict valid affordances and learn possible 3D human poses in indoor scenes, we need to understand the semantic and geometric structure of a scene as well as its potential interactions with a human.
NRMVS: Non-Rigid Multi-View Stereo
We show that creating a dense 4D structure from a few RGB images with non-rigid changes is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of these deformation estimates derived from the sparse views.
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
Depth sensing is crucial for 3D reconstruction and scene understanding.
Neural Inverse Rendering of an Indoor Scene from a Single Image
Inverse rendering aims to estimate physical attributes of a scene, e. g., reflectance, geometry, and lighting, from image(s).
PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image.
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data
We present an iterative overlap estimation technique to augment existing point cloud registration algorithms that can achieve high performance in difficult real-world situations where large pose displacement and non-overlapping geometry would otherwise cause traditional methods to fail.
Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures
Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Ranked #72 on
Monocular Depth Estimation
on KITTI Eigen split
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems.
Geometry-Aware Learning of Maps for Camera Localization
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking.
Ranked #5 on
Visual Localization
on Oxford RobotCar Full
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences.
Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting
We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors.
A Lightweight Approach for On-the-Fly Reflectance Estimation
In addition, we have created a large synthetic dataset, SynBRDF, which comprises a total of $500$K RGBD images rendered with a physically-based ray tracer under a variety of natural illumination, covering $5000$ materials and $5000$ shapes.
Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network
Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous.
Accelerated Generative Models for 3D Point Cloud Data
In this paper we introduce a method for constructing compact generative representations of PCD at multiple levels of detail.
