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Found 20 papers, 14 papers with code
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian
We propose a framework that can deform an object in a 2D image as it exists in 3D space.
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications.
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders.
PartGlot: Learning Shape Part Segmentation from Language Reference Games
We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language.
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.
CTRL-C: Camera calibration TRansformer with Line-Classification
In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which directly estimates the camera parameters from an image and a set of line segments.
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks.
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.
Joint Learning of 3D Shape Retrieval and Deformation
In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.
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.
DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces
Shape deformation is an important component in any geometry processing toolbox.
Graphics
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
Neural Geometric Parser for Single Image Camera Calibration
With the supervision of datasets consisting of the horizontal line and focal length of the images, our networks can be trained to estimate the same camera parameters.
Deformation-Aware 3D Model Embedding and Retrieval
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Ranked #16 on
3D Object Detection
on ScanNetV2
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes.
Learning Fuzzy Set Representations of Partial Shapes on Dual Embedding Spaces
Modeling relations between components of 3D objects is essential for many geometry editing tasks.
Graphics I.3.5
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes.
ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
The combinatorial nature of part arrangements poses another challenge, since the retrieval network is not a function: several complements can be appropriate for the same input.
Graphics I.3.5
