Search Results for author:
Found 27 papers, 9 papers with code
MV-Adapter: Multi-view Consistent Image Generation Made Easy
To efficiently model the 3D geometric knowledge within the adapter, we introduce innovative designs that include duplicated self-attention layers and parallel attention architecture, enabling the adapter to inherit the powerful priors of the pre-trained models to model the novel 3D knowledge.
MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation
This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image.
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates
We present SuperMat, a single-step framework that achieves high-quality material decomposition with one-step inference.
TEXGen: a Generative Diffusion Model for Mesh Textures
Instead, we focus on the fundamental problem of learning in the UV texture space itself.
SAMPart3D: Segment Any Part in 3D Objects
For flexibility, we distill scale-conditioned part-aware 3D features for 3D part segmentation at multiple granularities.
DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model
We introduce DreamCraft3D++, an extension of DreamCraft3D that enables efficient high-quality generation of complex 3D assets.
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics.
PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion
However, the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets.
Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers
Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models.
DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views.
Text-to-3D with Classifier Score Distillation
In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks.
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.
Ranked #2 on
Single-View 3D Reconstruction
on GSO
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas
Unlike generalizable radiance fields trained on perspective images, PanoGRF avoids the information loss from panorama-to-perspective conversion and directly aggregates geometry and appearance features of 3D sample points from each panoramic view based on spherical projection.
DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation
Automatic generation of fonts can be an important aid to typeface design.
VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesis
With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached an unprecedented level.
MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors
To address these issues, we present MBPTrack, which adopts a Memory mechanism to utilize past information and formulates localization in a coarse-to-fine scheme using Box Priors given in the first frame.
Joint Implicit Neural Representation for High-fidelity and Compact Vector Fonts
Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical scenarios.
CXTrack: Improving 3D Point Cloud Tracking with Contextual Information
Therefore, contextual information across two consecutive frames is crucial for effective object tracking.
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints
Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images.
Gradient-based Point Cloud Denoising with Uniformity
Point clouds captured by depth sensors are often contaminated by noises, obstructing further analysis and applications.
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding
Is the center position fully capable of representing a pixel?
NeRF-SR: High-Quality Neural Radiance Fields using Supersampling
We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs.
NeRFReN: Neural Radiance Fields with Reflections
Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields.
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have a computer system automatically generate photo-realistic images that adhere to the input content.
Learning Implicit Glyph Shape Representation
In this paper, we present a novel implicit glyph shape representation, which models glyphs as shape primitives enclosed by quadratic curves, and naturally enables generating glyph images at arbitrary high resolutions.
TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields
Place recognition plays an essential role in the field of autonomous driving and robot navigation.
Ranked #4 on
3D Place Recognition
on CS-Campus3D
Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches
We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users.
