Search Results for author:
Found 7 papers, 4 papers with code
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress.
T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF.
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes.
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
O$^2$-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos
In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS.
Learning to compose 6-DoF omnidirectional videos using multi-sphere images
The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition.
