Search Results for author:
Found 14 papers, 4 papers with code
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
This paper studies implicit surface reconstruction leveraging differentiable ray casting.
3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue
However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows.
DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization
Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions.
Ranked #9 on
Image Harmonization
on iHarmony4
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
NeRF-Editing: Geometry Editing of Neural Radiance Fields
In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network.
Modeling Indirect Illumination for Inverse Rendering
The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials.
Data-Free Evaluation of User Contributions in Federated Learning
We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Adapting semantic segmentation models to new domains is an important but challenging problem.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database.
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
3D Object Reconstruction From A Single Image
3D Shape Retrieval
+4
Distributed Optimization over Block-Cyclic Data
We consider practical data characteristics underlying federated learning, where unbalanced and non-i. i. d.
Secure Federated Submodel Learning
Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.
