Search Results for author:
Found 24 papers, 6 papers with code
Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency
Our key contribution is the introduction of generation rate, which corresponds to the local deformation of manifold over time around an image component.
Deep-PE: A Learning-Based Pose Evaluator for Point Cloud Registration
In the realm of point cloud registration, the most prevalent pose evaluation approaches are statistics-based, identifying the optimal transformation by maximizing the number of consistent correspondences.
ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models
We show that the space spanned by the combination of dimensions and attributes is insufficiently sampled by existing training scheme of diffusion generative models, causing degraded test time performance.
NeurCross: A Self-Supervised Neural Approach for Representing Cross Fields in Quad Mesh Generation
Quadrilateral mesh generation plays a crucial role in numerical simulations within Computer-Aided Design and Engineering (CAD/E).
CWF: Consolidating Weak Features in High-quality Mesh Simplification
In mesh simplification, common requirements like accuracy, triangle quality, and feature alignment are often considered as a trade-off.
NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature
Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge.
OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer
On one hand, we introduce a soft matching mechanism, facilitating the propagation of potentially valuable correspondences from coarse to fine levels.
For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.
Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian
Our approach enforces the Hessian of the neural implicit function to have a zero determinant for points near the surface.
Guided Linear Upsampling
We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing.
A Task-driven Network for Mesh Classification and Semantic Part Segmentation
With the rapid development of geometric deep learning techniques, many mesh-based convolutional operators have been proposed to bridge irregular mesh structures and popular backbone networks.
Laplacian2Mesh: Laplacian-Based Mesh Understanding
Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation.
Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction
We conducted extensive experiments on various benchmarks, including synthetic scans and real scans.
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
Ranked #3 on
Semantic Segmentation
on Stanford2D3D - RGBD
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations.
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace
Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts.
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization.
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds
Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene.
Unsupervised Learning of Intrinsic Structural Representation Points
The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures.
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
DifNet: Semantic Segmentation by Diffusion Networks
Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions.
DiDA: Disentangled Synthesis for Domain Adaptation
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.
Neuron-level Selective Context Aggregation for Scene Segmentation
Contextual information provides important cues for disambiguating visually similar pixels in scene segmentation.
Synthesizing Training Images for Boosting Human 3D Pose Estimation
Human 3D pose estimation from a single image is a challenging task with numerous applications.
