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Found 64 papers, 25 papers with code
ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
Our framework consists of steps of solving the feature `dropout' problem when indistinctive structures, noise or large image distortion exists, and of rapidly recognizing and joining common features located in different subsequences.
Robust Keyframe-based Dense SLAM with an RGB-D Camera
In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene.
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM
However, jointly using visual and inertial measurements to optimize SLAM objective functions is a problem of high computational complexity.
Quantum tensor singular value decomposition with applications to recommendation systems
In this paper, we present a quantum singular value decomposition algorithm for third-order tensors inspired by the classical algorithm of tensor singular value decomposition (t-svd) and then extend it to order-$p$ tensors.
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints
Most of existing methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises.
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone
The proposed mesh generation module incrementally fuses each estimated keyframe depth map to an online dense surface mesh, which is useful for achieving realistic AR effects such as occlusions and collisions.
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks
To suit our network to self-supervised learning, we design several novel loss functions that utilize the inherent properties of LiDAR point clouds.
Super interference fringes of two-photon photoluminescence in individual Au nanoparticles: the critical role of the intermediate state
Here, we presented two-photon photoluminescence (TPPL) measurements on individual Au nanobipyramids (AuNP) to reveal their ultrafast dynamics by two-pulse excitation on a global time scale ranging from sub-femtosecond to tens of picoseconds.
Optics Mesoscale and Nanoscale Physics Quantum Physics
You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking
In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames.
Detecting quantum entanglement with unsupervised learning
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks.
LIFE: Lighting Invariant Flow Estimation
However, local image contents are inevitably ambiguous and error-prone during the cross-image feature matching process, which hinders downstream tasks.
VS-Net: Voting with Segmentation for Visual Localization
To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks.
Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering
In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene.
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
In this work, we propose a novel neural implicit representation for the human body, which is fully differentiable and optimizable with disentangled shape and pose latent spaces.
Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming
Smart weeding systems to perform plant-specific operations can contribute to the sustainability of agriculture and the environment.
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.
Efficient Quantum Feature Extraction for CNN-based Learning
Recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as general function approximators.
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition
We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features.
Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling
The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans.
Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation
HybridTracker performs pixel tracker and instance tracker in parallel to obtain the association matrices, which are fused into a matching matrix.
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization
The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses.
Ranked #1 on
6D Pose Estimation using RGB
on LineMOD
Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects
We, as human beings, can understand and picture a familiar scene from arbitrary viewpoints given a single image, whereas this is still a grand challenge for computers.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
OnePose: One-Shot Object Pose Estimation without CAD Models
We propose a new method named OnePose for object pose estimation.
PVO: Panoptic Visual Odometry
We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information.
VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM
Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures.
Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation
Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications.
DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM
We present a novel dual-flow representation of scene motion that decomposes the optical flow into a static flow field caused by the camera motion and another dynamic flow field caused by the objects' movements in the scene.
NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing
Very recently neural implicit rendering techniques have been rapidly evolved and shown great advantages in novel view synthesis and 3D scene reconstruction.
Vox-Surf: Voxel-based Implicit Surface Representation
Virtual content creation and interaction play an important role in modern 3D applications such as AR and VR.
NeuralMarker: A Framework for Learning General Marker Correspondence
We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker.
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image
Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection, etc.
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
Existing inverse rendering combined with neural rendering methods can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based neural rendering method and can extend its application to room-scale scenes.
Generative Category-Level Shape and Pose Estimation with Semantic Primitives
Then, by using a SIM(3)-invariant shape descriptor, we gracefully decouple the shape and pose of an object, thus supporting latent shape optimization of target objects in arbitrary poses.
Ranked #2 on
6D Pose Estimation using RGBD
on REAL275
Hierarchical and Progressive Image Matting
In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input.
Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation
In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods.
Improving Feature-based Visual Localization by Geometry-Aided Matching
We apply GAM to a new hierarchical visual localization pipeline and show that GAM can effectively improve the robustness and accuracy of localization.
Deep Active Contours for Real-time 6-DoF Object Tracking
Specifically, given an initial pose, we project the object model to the image plane to obtain the initial contour and use a lightweight network to predict how the contour should move to match the true object boundary, which provides the gradients to optimize the object pose.
DPS-Net: Deep Polarimetric Stereo Depth Estimation
Stereo depth estimation usually struggles to deal with textureless scenes for both traditional and learning-based methods due to the inherent dependence on image correspondence matching.
Clothed Human Performance Capture With a Double-Layer Neural Radiance Fields
Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses.
EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images
Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two.
PATS: Patch Area Transportation with Subdivision for Local Feature Matching
However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs.
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
BlinkSim consists of a configurable rendering engine and a flexible engine for event data simulation.
SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field
Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories.
Long-term Visual Localization with Mobile Sensors
Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes.
A bioinspired three-stage model for camouflaged object detection
Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues.
Robust 3D-aware Object Classification via Discriminative Render-and-Compare
Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image.
DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation
In addition to the scene generation, the final part of DiffInDScene can be used as a post-processing module to refine the 3D reconstruction results from multi-view stereo.
Generating Images with 3D Annotations Using Diffusion Models
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing
Recently, Neural Radiance Fields (NeRF) has exhibited significant success in novel view synthesis, surface reconstruction, etc.
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on
Animal Pose Estimation
on Animal3D
Graph-based Asynchronous Event Processing for Rapid Object Recognition
Different from traditional video cameras, event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes.
Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor
Specifically, we propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor which drives the optimization by comparing with the raw sensor inputs.
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM
In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone.
RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems.
Hard Label Black Box Node Injection Attack on Graph Neural Networks
In this work, we will propose a non-targeted Hard Label Black Box Node Injection Attack on Graph Neural Networks, which to the best of our knowledge, is the first of its kind.
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement
Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow.
On Metzler positive systems on hypergraphs
To be more precise, in this paper, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors.
Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
However, conventional AFM scanning struggles to reconstruct complex 3D micro-/nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts.
On discrete-time polynomial dynamical systems on hypergraphs
Afterward, we extend our results to non-homogeneous polynomial systems on non-uniform hypergraphs.
Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps
In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense tracking and mapping system that seamlessly fuses neural implicit representations with traditional volumetric fusion techniques.
CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field
Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM).
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars.
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal
This paper tackles the intricate challenge of object removal to update the radiance field using the 3D Gaussian Splatting.
