Search Results for author:
Found 47 papers, 18 papers with code
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation.
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Images from video generative models are more suitable for multi-view generation because the underlying network architecture that generates them employs a temporal module to enforce frame consistency.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction.
AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image.
Improving Adversarial Transferability by Stable Diffusion
Various techniques have emerged to enhance the transferability of adversarial attacks for the black-box scenario.
QuantEase: Optimization-based Quantization for Language Models
Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.
Fine-grained Text-Video Retrieval with Frozen Image Encoders
In the second stage, we propose a novel decoupled video text cross attention module to capture fine-grained multimodal information in spatial and temporal dimensions.
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of available datasets.
3D Former: Monocular Scene Reconstruction with 3D SDF Transformers
In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation.
Towards Robust Video Instance Segmentation with Temporal-Aware Transformer
In this paper, we observe the temporal information is important as well and we propose TAFormer to aggregate spatio-temporal features both in transformer encoder and decoder.
Linguistic Query-Guided Mask Generation for Referring Image Segmentation
Referring image segmentation aims to segment the image region of interest according to the given language expression, which is a typical multi-modal task.
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.
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.
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
QuadTree Attention for Vision Transformers
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency.
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.
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.
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.
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 #8 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.
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.
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
UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation
Besides, we also designed a more effective fusion module for our fusion scheme.
Ranked #1 on
Depth Estimation
on Matterport3D
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.
Self-Supervised Human Depth Estimation from Monocular Videos
Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data.
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
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 #12 on
Point Clouds
on Tanks and Temples
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.
Matchable Image Retrieval by Learning from Surface Reconstruction
Convolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction.
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
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction.
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space.
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.
Progressive Large Scale-Invariant Image Matching in Scale Space
In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views.
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus
In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation.
Parallel Structure from Motion from Local Increment to Global Averaging
In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel.
A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification
We propose a system that finds text in natural scenes using a variety of cues.
Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo
To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters.
Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms
We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO).
Local Readjustment for High-Resolution 3D Reconstruction
To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details.
