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Found 84 papers, 28 papers with code
DVI-SLAM: A Dual Visual Inertial SLAM Network
Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress.
Learning Photometric Feature Transform for Free-form Object Scan
We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are fed to a multi-view stereo method to enhance 3D reconstruction.
High-Resolution Volumetric Reconstruction for Clothed Humans
We present a novel method for reconstructing clothed humans from a sparse set of, e. g., 1 to 6 RGB images.
Compact Real-time Radiance Fields with Neural Codebook
Reconstructing neural radiance fields with explicit volumetric representations, demonstrated by Plenoxels, has shown remarkable advantages on training and rendering efficiency, while grid-based representations typically induce considerable overhead for storage and transmission.
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Panoramic image enables deeper understanding and more holistic perception of $360^\circ$ surrounding environment, which can naturally encode enriched scene context information compared to standard perspective image.
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
Learning Optical Flow from Event Camera with Rendered Dataset
Previous datasets are created by either capturing real scenes by event cameras or synthesizing from images with pasted foreground objects.
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning
To answer this question, this paper makes a beginning effort by proposing a shallow binary feature module (SBFM for short), which can be integrated into any popular backbone.
Dense RGB SLAM with Neural Implicit Maps
To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder.
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function.
Minimum Latency Deep Online Video Stabilization
In this work, we adopt recent off-the-shelf high-quality deep motion models for motion estimation to recover the camera trajectory and focus on the latter two steps.
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization
State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consuming several gigabytes of storage per scene.
Streaming Radiance Fields for 3D Video Synthesis
Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Unity GUI is also provided to generate synthetic hand data with user-defined settings, e. g., pose, camera, background, lighting, textures, and accessories.
Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects
Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks.
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated.
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
Efficient Virtual View Selection for 3D Hand Pose Estimation
3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand.
Ranked #1 on
Hand Pose Estimation
on ICVL Hands
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
In this work, for the first time, we enable autoregressive modeling of implicit avatars.
A Real World Dataset for Multi-view 3D Reconstruction
We present a dataset of 998 3D models of everyday tabletop objects along with their 847, 000 real world RGB and depth images.
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation
While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization.
Ranked #1 on
Depth Prediction
on Matterport3D
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting.
QuadTree Attention for Vision Transformers
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency.
GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings
Spotting graphical symbols from the computer-aided design (CAD) drawings is essential to many industrial applications.
RCP: Recurrent Closest Point for Point Cloud
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
Neural Window Fully-Connected CRFs for Monocular Depth Estimation
Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed.
SceneSqueezer: Learning To Compress Scene for Camera Relocalization
Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image.
Learning To Zoom Inside Camera Imaging Pipeline
To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model.
GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification
Our GB-CosFace introduces an adaptive global boundary to determine whether two face samples belong to the same identity so that the optimization objective is aligned with the testing process from the perspective of open set classification.
End-to-End Rotation Averaging With Multi-Source Propagation
These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations.
FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting
The proposed CNN-GCN method achieved state-of-the-art (SOTA) performance on the task of semantic symbol spotting, and help us build a baseline network for the panoptic symbol spotting task.
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies.
Ranked #5 on
Lane Detection
on CurveLanes
(using extra training data)
Stereo Matching by Self-supervision of Multiscopic Vision
Self-supervised learning for depth estimation possesses several advantages over supervised learning.
Riggable 3D Face Reconstruction via In-Network Optimization
This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations.
Learning Camera Localization via Dense Scene Matching
We present a new method for scene agnostic camera localization using dense scene matching (DSM), where a cost volume is constructed between a query image and a scene.
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
Learning Efficient Photometric Feature Transform for Multi-view Stereo
We present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction.
AR Mapping: Accurate and Efficient Mapping for Augmented Reality
Secondly, we propose an AR mapping pipeline which takes the input from the scanning device and produces accurate AR Maps.
DRO: Deep Recurrent Optimizer for Video to Depth
There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Ranked #1 on
Unsupervised Person Re-Identification
on PersonX
Interacting Two-Hand 3D Pose and Shape Reconstruction From Single Color Image
In this paper, we propose a novel deep learning framework to reconstruct 3D hand poses and shapes of two interacting hands from a single color image.
MeshMVS: Multi-View Stereo Guided Mesh Reconstruction
Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process.
Interpretable Foreground Object Search As Knowledge Distillation
As for the proposed method, we first train a foreground encoder to learn representations of interchangeable foregrounds.
Self-Supervised Human Depth Estimation from Monocular Videos
Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data.
LSM: Learning Subspace Minimization for Low-level Vision
We study the energy minimization problem in low-level vision tasks from a novel perspective.
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds.
Ranked #5 on
Point Cloud Registration
on 3DMatch Benchmark
Channel Equilibrium Networks for Learning Deep Representation
Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.
Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection
Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition.
Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials
From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera.
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.
Ranked #7 on
Point Clouds
on Tanks and Temples
Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation
We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images.
A Neural Network for Detailed Human Depth Estimation from a Single Image
To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively.
Channel Equilibrium Networks
However, over-sparse CNNs have many collapsed channels (i. e. many channels with undesired zero values), impeding their learning ability.
Learning Guided Convolutional Network for Depth Completion
It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.
BA-Net: Dense Bundle Adjustment Networks
The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.
Batch DropBlock Network for Person Re-identification and Beyond
In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch.
Ranked #8 on
Person Re-Identification
on Market-1501-C
BA-Net: Dense Bundle Adjustment Network
The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.
Very Large-Scale Global SfM by Distributed Motion Averaging
This work proposes a divide-and-conquer framework to solve very large global SfM at the scale of millions of images.
Polarimetric Dense Monocular SLAM
This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera.
Faces as Lighting Probes via Unsupervised Deep Highlight Extraction
We present a method for estimating detailed scene illumination using human faces in a single image.
Sparsely Aggregated Convolutional Networks
We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers.
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods.
Polarimetric Multi-View Stereo
Multi-view stereo relies on feature correspondences for 3D reconstruction, and thus is fundamentally flawed in dealing with featureless scenes.
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models from images.
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.
Ranked #2 on
Image-to-Image Translation
on Aerial-to-Map
Hand3D: Hand Pose Estimation using 3D Neural Network
We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image.
Joint Hand Detection and Rotation Estimation by Using CNN
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
Attribute Recognition from Adaptive Parts
Previous part-based attribute recognition approaches perform part detection and attribute recognition in separate steps.
Automatic Fence Segmentation in Videos of Dynamic Scenes
We present a fully automatic approach to detect and segment fence-like occluders from a video clip.
A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions.
Towards Building an RGBD-M Scanner
We present a portable device to capture both shape and reflectance of an indoor scene.
Global Structure-From-Motion by Similarity Averaging
Depth images help to upgrade an essential matrix to a similarity transformation, which can determine the scale of relative translation.
Simultaneous Video Defogging and Stereo Reconstruction
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence.
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation.
SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization
We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization.
A New Perspective on Material Classification and Ink Identification
The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials.
FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps
We analyze the self-similarity of the guide image to generate a set of allowable local transformations and apply them to the input image.
Calibrating Photometric Stereo by Holistic Reflectance Symmetry Analysis
Under unknown directional lighting, the uncalibrated Lambertian photometric stereo algorithm recovers the shape of a smooth surface up to the generalized bas-relief (GBR) ambiguity.
Image Matting with Local and Nonlocal Smooth Priors
This nonlocal smooth prior and the well known local smooth prior from matting Laplacian complement each other.
Multi-view Photometric Stereo with Spatially Varying Isotropic Materials
We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique that works for general isotropic materials.
Sketch2Photo: Internet Image Montage
The composed picture is generated by seamlessly stitching several photographs in agreement with the sketch and text labels; these are found by searching the Internet.
