Search Results for author:
Found 32 papers, 6 papers with code
Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration
In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction.
Context-Aware Integration of Language and Visual References for Natural Language Tracking
Tracking by natural language specification (TNL) aims to consistently localize a target in a video sequence given a linguistic description in the initial frame.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
2L3: Lifting Imperfect Generated 2D Images into Accurate 3D
Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures.
In-Hand 3D Object Reconstruction from a Monocular RGB Video
We evaluate our approach on HO3D and HOD datasets and demonstrate that it outperforms the state-of-the-art methods in terms of reconstruction surface quality, with an improvement of $52\%$ on HO3D and $20\%$ on HOD.
Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning
In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs).
Expressibility-induced Concentration of Quantum Neural Tangent Kernels
In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero.
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild
Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding.
Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields
With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation.
Evaluation of Multi-indicator And Multi-organ Medical Image Segmentation Models
As a result, MIMO offers detailed information on the segmentation of each organ in each sample, thereby aiding developers in analyzing and improving the model.
I$^2$-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs).
Composite Optimization Algorithms for Sigmoid Networks
In this paper, we use composite optimization algorithms to solve sigmoid networks.
Metadata-Based RAW Reconstruction via Implicit Neural Functions
Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance.
I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
Further, we propose to decompose the neural radiance field into spatially-varying material of the scene as a neural field through surface-based, differentiable Monte Carlo raytracing and emitter semantic segmentations, which enables physically based and photorealistic scene relighting and editing applications.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects.
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model
Continual pretraining is a popular way of building a domain-specific pretrained language model from a general-domain language model.
Pixel-Aligned Non-parametric Hand Mesh Reconstruction
The decoder aggregate both local image features in pixels and geometric features in vertices.
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses.
ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions
However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images.
mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar
The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected.
NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations
In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one.
Few-shot learning with improved local representations via bias rectify module
Recent approaches based on metric learning have achieved great progress in few-shot learning.
Analysis of Regularized Learning for Linear-functional Data in Banach Spaces
In the convergence theorems, we show the convergence of the approximate solutions to the exact solutions by the weak* topology of the Banach space.
Sample Complexity of Learning Parametric Quantum Circuits
Quantum computers hold unprecedented potentials for machine learning applications.
Geometry-based Distance Decomposition for Monocular 3D Object Detection
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Birds Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics.
Ranked #15 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Optimal Retirement Time and Consumption with the Variation in Habitual Persistence
In this paper, we study the individual's optimal retirement time and optimal consumption under habitual persistence.
The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization
Realtime perceptual and interaction capabilities in mixed reality require a range of 3D tracking problems to be solved at low latency on resource-constrained hardware such as head-mounted devices.
Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network
The proposed method leverages the state-of-the-art hand pose estimators based on Convolutional Neural Networks to facilitate feature learning, while it models the multiple modes in a two-level hierarchy to reconcile single-valued and multi-valued mapping in its output.
The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation
We present the 2017 Hands in the Million Challenge, a public competition designed for the evaluation of the task of 3D hand pose estimation.
BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation
In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO).
Solving Support Vector Machines in Reproducing Kernel Banach Spaces with Positive Definite Functions
In this paper we solve support vector machines in reproducing kernel Banach spaces with reproducing kernels defined on nonsymmetric domains instead of the traditional methods in reproducing kernel Hilbert spaces.
