Search Results for author:
Found 13 papers, 6 papers with code
StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields
In this paper, we introduce StyleDyRF, a method that represents the 4D feature space by deforming a canonical feature volume and learns a linear style transformation matrix on the feature volume in a data-driven fashion.
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description.
Focus on Local Regions for Query-based Object Detection
We improve the self-attention by isolating connections between irrelevant objects that makes it focus on local regions but not global regions.
CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo
The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions.
PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations.
A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation.
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations.
Semi-supervised Deep Multi-view Stereo
For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap.
V4D: Voxel for 4D Novel View Synthesis
The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task.
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning
To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation.
Digging into Uncertainty in Self-supervised Multi-view Stereo
Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS).
