Search Results for author:
Found 39 papers, 8 papers with code
Deep Surface Normal Estimation on the 2-Sphere with Confidence Guided Semantic Attention
We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with a low-quality depth channel.
Semantic Human Mesh Reconstruction with Textures
SHERT applies semantic- and normal-based sampling between the detailed surface (e. g. mesh and SDF) and the corresponding SMPL-X model to obtain a partially sampled semantic mesh and then generates the complete semantic mesh by our specifically designed self-supervised completion and refinement networks.
On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy
This paper addresses the projection error function of 3D Gaussian Splatting, commencing with the residual error from the first-order Taylor expansion of the projection function.
360-GS: Layout-guided Panoramic Gaussian Splatting For Indoor Roaming
This technique typically takes perspective images as input and optimizes a set of 3D elliptical Gaussians by splatting them onto the image planes, resulting in 2D Gaussians.
FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functions
Implicit Neural Representation (INR), which utilizes a neural network to map coordinate inputs to corresponding attributes, is causing a revolution in the field of signal processing.
Manifold Path Guiding for Importance Sampling Specular Chains
We verify that importance sampling the seed chain in the continuous space reaches the goal of importance sampling the discrete admissible specular chain.
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama.
Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields
Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images.
Symmetric Shape-Preserving Autoencoder for Unsupervised Real Scene Point Cloud Completion
Extensive experiments and comparisons demonstrate our superiority and generalization and show that our method achieves state-of-the-art performance on unsupervised completion of real scene objects.
UHDNeRF: Ultra-High-Definition Neural Radiance Fields
We propose UHDNeRF, a new framework for novel view synthesis on the challenging ultra-high-resolution (e. g., 4K) real-world scenes.
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner.
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.
AGConv: Adaptive Graph Convolution on 3D Point Clouds
In this paper, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis.
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.
GLPanoDepth: Global-to-Local Panoramic Depth Estimation
In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image.
Ranked #7 on
Depth Estimation
on Stanford2D3D Panoramic
Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation
Encoder-decoder models have been widely used in RGBD semantic segmentation, and most of them are designed via a two-stream network.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Temporal Alignment Prediction for Few-Shot Video Classification
In order to obtain the similarity of a pair of videos, we predict the alignment scores between all pairs of temporal positions in the two videos with the temporal alignment prediction function.
A Transductive Maximum Margin Classifier for Few-Shot Learning
We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC).
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition
We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition.
Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Multi-Label Classification
Multi-label Image Recognition with Partial Labels
Rendering Discrete Participating Media with Geometrical Optics Approximation
We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles.
Graphics Optics
A Unified Light Framework for Real-time Fault Detection of Freight Train Images
Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements.
Hierarchical Disentangled Representation Learning for Outdoor Illumination Estimation and Editing
However, naively compressing an outdoor panorama into a low-dimensional latent vector, as existing models have done, causes two major problems.
Tensor Composition Net for Visual Relationship Prediction
We present a novel Tensor Composition Net (TCN) to predict visual relationships in images.
Hierarchical Context Embedding for Region-based Object Detection
To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues.
Image Stitching Based on Planar Region Consensus
We specifically design a new module to make fully use of existing semantic segmentation networks to accommodate planar segmentation.
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science.
Ranked #7 on
Formation Energy
on QM9
Affinity Fusion Graph-based Framework for Natural Image Segmentation
The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels.
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
To the best of our knowledge, we are the first to enhance the facial attractiveness with GANs in both geometry and appearance aspects.
Hybrid Models for Open Set Recognition
We propose the OpenHybrid framework, which is composed of an encoder to encode the input data into a joint embedding space, a classifier to classify samples to inlier classes, and a flow-based density estimator to detect whether a sample belongs to the unknown category.
Multi-Label Image Recognition with Graph Convolutional Networks
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Ranked #12 on
Multi-Label Classification
on PASCAL VOC 2007
Viewpoint Selection for Photographing Architectures
Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality.
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models
In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster.
Learning to Generate Posters of Scientific Papers
Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized.
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking
The proposed DNBS representation is applied to object tracking through SSD-based template matching.
Multi-view Metric Learning for Multi-view Video Summarization
Traditional methods on video summarization are designed to generate summaries for single-view video records; and thus they cannot fully exploit the redundancy in multi-view video records.
