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Found 20 papers, 8 papers with code
DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics.
Deep Anchored Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks.
ClusterSLAM: A SLAM Backend for Simultaneous Rigid Body Clustering and Motion Estimation
We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments.
Shallow2Deep: Indoor Scene Modeling by Single Image Understanding
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects.
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning
In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models.
DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors
Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors.
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds.
Subdivision-Based Mesh Convolution Networks
Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach.
Layered Controllable Video Generation
In the first stage, with the rich set of losses and dynamic foreground size prior, we learn how to separate the frame into foreground and background layers and, conditioned on these layers, how to generate the next frame using VQ-VAE generator.
CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene
We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions.
Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization
We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes by synchronizing the maps relating learned functions defined on the point clouds.
Dynamic 3D Scene Analysis by Point Cloud Accumulation
Compared to state-of-the-art scene flow estimators, our proposed approach aims to align all 3D points in a common reference frame correctly accumulating the points on the individual objects.
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model.
DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion
We propose a factorization that models independent part style and part configuration distributions and presents a novel cross-diffusion network that enables us to generate coherent and plausible shapes under our proposed factorization.
Revisiting Acceptability Judgements
We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language.
Neural Kernel Surface Reconstruction
We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud.
INVE: Interactive Neural Video Editing
We present Interactive Neural Video Editing (INVE), a real-time video editing solution, which can assist the video editing process by consistently propagating sparse frame edits to the entire video clip.
XCube ($\mathcal{X}^3$): Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
In addition to unconditional generation, we show that our model can be used to solve a variety of tasks such as user-guided editing, scene completion from a single scan, and text-to-3D.
Approximately Piecewise E(3) Equivariant Point Networks
Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability.
