Search Results for author:
Found 19 papers, 8 papers with code
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image.
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment.
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images.
Point2Skeleton: Learning Skeletal Representations from Point Clouds
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds.
Modeling 3D Shapes by Reinforcement Learning
We explore how to enable machines to model 3D shapes like human modelers using deep reinforcement learning (RL).
SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications.
Adaptive Surface Normal Constraint for Depth Estimation
We present a novel method for single image depth estimation using surface normal constraints.
Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans
We present a novel approach to align partial 3D reconstructions which may not have substantial overlap.
Adaptive Compact Attention For Few-shot Video-to-video Translation
This paper proposes an adaptive compact attention model for few-shot video-to-video translation.
Learnable Motion Coherence for Correspondence Pruning
We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian.
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies
The key to our method is the introduction of a new sign, the NULL sign, in addition to the conventional in and out labels.
To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden
In this paper, we build on the theory of cumulative disadvantage and examine whether the accumulated use of social assistance over the life course is associated with an increased risk of future social assistance recipiency.
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
We further demonstrate the generalizability of our method on hand mesh recovery.
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
Wonder3D: Single Image to 3D using Cross-Domain Diffusion
In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.
Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization.
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context.
