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Found 36 papers, 20 papers with code
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs.
Single-View Reconstruction via Joint Analysis of Image and Shape Collections
We present an approach to automatic 3D reconstruction of objects depicted in Web images.
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions
Deep learning has made significant impacts on multi-view stereo systems.
HPNet: Deep Primitive Segmentation Using Hybrid Representations
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
Attention-guided Temporally Coherent Video Object Matting
Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion.
StruMonoNet: Structure-Aware Monocular 3D Prediction
Monocular 3D prediction is one of the fundamental problems in 3D vision.
Learning View Selection for 3D Scenes
Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention.
Ranked #7 on
Skeleton Based Action Recognition
on N-UCLA
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
3D Point Cloud Classification
3D Point Cloud Linear Classification
+3
E-CIR: Event-Enhanced Continuous Intensity Recovery
A camera begins to sense light the moment we press the shutter button.
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
Reconstructing 3D objects is an important computer vision task that has wide application in AR/VR.
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Our key insight is to copy and deform patches from the partial input to complete missing regions.
Neural Volumetric Mesh Generator
We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures.
Pose Synchronization Under Multiple Pair-Wise Relative Poses
The first step performs diffusion and clustering to compute the candidate poses of the input objects.
DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
We additionally show that our approach easily extends to video super-resolution when combined with recent advances in implicit neural representation.
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors.
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision.
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
GenCorres addresses this issue by learning an implicit generator from the input shapes, which provides intermediate shapes between two arbitrary shapes.
Jigsaw: Learning to Assemble Multiple Fractured Objects
Our framework consists of four components: (1) front-end point feature extractor with attention layers, (2) surface segmentation to separate fracture and original parts, (3) multi-parts matching to find correspondences among fracture surface points, and (4) robust global alignment to recover the global poses of the pieces.
Multi-View Representation is What You Need for Point-Cloud Pre-Training
Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction.
Detector-Free Structure from Motion
We propose a new detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints, while solving the multi-view inconsistency issue of detector-free matchers.
Rapid Flood Inundation Forecast Using Fourier Neural Operator
Flood inundation forecast provides critical information for emergency planning before and during flood events.
Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On
Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process.
What is the Best Automated Metric for Text to Motion Generation?
Our findings indicate that none of the metrics currently used for this task show even a moderate correlation with human judgments on a sample level.
LEAP: Liberate Sparse-view 3D Modeling from Camera Poses
Are camera poses necessary for multi-view 3D modeling?
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers
In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.
UGG: Unified Generative Grasping
Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information.
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities.
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.
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes Using Pre-Trained Text-to-Image Models
In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts.
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
