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Found 43 papers, 11 papers with code
RobustFusion: Human Volumetric Capture with Data-driven Visual Cues using a RGBD Camera
To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios.
XScale-NVS: Cross-Scale Novel View Synthesis with Hash Featurized Manifold
We also introduce a novel dataset, namely GigaNVS, to benchmark cross-scale, high-resolution novel view synthesis of realworld large-scale scenes.
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios.
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning
Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure.
PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis
This paper proposes a method for fast scene radiance field reconstruction with strong novel view synthesis performance and convenient scene editing functionality.
SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation
To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points.
ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations
Instead of directly using the model inference way, i. e., mean-shift clustering, to generate the pseudo labels, we propose to use k-means with fixed initial seeds: the annotated points.
RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent
Based on this insight, we introduce EffLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse view images in real time.
Crowd3D: Towards Hundreds of People Reconstruction from a Single Image
However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution.
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
DartBlur: Privacy Preservation With Detection Artifact Suppression
Facial information is particularly sensitive in this regard.
Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering
In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering.
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation
Based on INS-Conv, an online joint 3D semantic and instance segmentation pipeline is proposed, reaching an inference speed of 15 FPS on GPU and 10 FPS on CPU.
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream
To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions.
Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network
In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques.
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models.
Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications
The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling.
LEAD: LiDAR Extender for Autonomous Driving
In this paper, we propose LEAD, i. e., LiDAR Extender for Autonomous Driving, to extend the MEMS LiDAR by coupled image w. r. t both FoV and range.
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons.
Spatial-Angular Attention Network for Light Field Reconstruction
In this paper, we propose a spatial-angular attention network to perceive correspondences in the light field non-locally, and reconstruction high angular resolution light field in an end-to-end manner.
Zoom in to the details of human-centric videos
Presenting high-resolution (HR) human appearance is always critical for the human-centric videos.
SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis
Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with the repeating pattern.
MulayCap: Multi-layer Human Performance Capture Using A Monocular Video Camera
We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning.
OccuSeg: Occupancy-aware 3D Instance Segmentation
3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days.
Ranked #1 on
3D Instance Segmentation
on SceneNN
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both.
EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera
The high frame rate is a critical requirement for capturing fast human motions.
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction
We then propose a novel network architecture for the LapEPI structure, termed as LapEPI-net.
SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization
Inspired by the conditional integration idea in classical control society, we propose SPI-Optimizer, an integral-Separated PI controller based optimizer WITHOUT introducing extra hyperparameter.
RegNet: Learning the Optimization of Direct Image-to-Image Pose Registration
Direct image-to-image alignment that relies on the optimization of photometric error metrics suffers from limited convergence range and sensitivity to lighting conditions.
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs
We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs).
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap x8.
Beyond SIFT using Binary features for Loop Closure Detection
The proposed system originates from our previous work Multi-Index hashing for Loop closure Detection (MILD), which employs Multi-Index Hashing (MIH)~\cite{greene1994multi} for Approximate Nearest Neighbor (ANN) search of binary features.
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model.
MILD: Multi-Index hashing for Loop closure Detection
Loop Closure Detection (LCD) has been proved to be extremely useful in global consistent visual Simultaneously Localization and Mapping (SLAM) and appearance-based robot relocalization.
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.
Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users.
Deep Learning for Surface Material Classification Using Haptic And Visual Information
When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface.
Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation
Finally, the optimization problem enables us to take advantage of state-of-the-art fully convolutional network structure for the implementation of the above encoders and decoder.
Separable Kernel for Image Deblurring
In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.
