Search Results for author:
Found 32 papers, 19 papers with code
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.
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.
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 #27 on
3D Object Detection
on ScanNetV2
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.
Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes
To handle the shape complexity and interaction context between two hands, Im2Hands models the occupancy volume of two hands - conditioned on an RGB image and coarse 3D keypoints - by two novel attention-based modules responsible for (1) initial occupancy estimation and (2) context-aware occupancy refinement, respectively.
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.
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.
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.
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.
ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations
In this work, we aim to facilitate the task of editing the geometry of 3D models through the use of natural language.
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.
LADIS: Language Disentanglement for 3D Shape Editing
Additionally, to measure edit locality, we define a new metric that we call part-wise edit precision.
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
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces
Shape deformation is an important component in any geometry processing toolbox.
Graphics
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.
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.
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
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.
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.
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.
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.
Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated framework.
SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation
We present a cascaded diffusion model based on a part-level implicit 3D representation.
Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search
We propose a novel framework for finding a set of tight bounding boxes of a 3D shape via over-segmentation and iterative merging and refinement.
Posterior Distillation Sampling
PDS matches the stochastic latents of the source and the target, enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity.
As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors
To better preserve the identity of the edited mesh, we fine-tune our 2D diffusion model with LoRA.
PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation.
ReGround: Improving Textual and Spatial Grounding at No Cost
Our analysis of a pretrained image diffusion model that integrates gated self-attention into the U-Net reveals that spatial grounding often outweighs textual grounding due to the sequential flow from gated self-attention to cross-attention.
SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
We introduce a general framework for generating diverse visual content, including ambiguous images, panorama images, mesh textures, and Gaussian splat textures, by synchronizing multiple diffusion processes.
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion
Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object.
