Search Results for author:
Found 9 papers, 7 papers with code
GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement
Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
TensoIR: Tensorial Inverse Rendering
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields.
Robust Point Cloud Segmentation with Noisy Annotations
To handle boundary-level label noise, we also propose a variant ``PNAL-boundary " with a progressive boundary label cleaning strategy.
3D Question Answering
To this end, we propose a novel transformer-based 3DQA framework "3DQA-TR", which consists of two encoders for exploiting the appearance and geometry information, respectively.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
Learning with Noisy Labels for Robust Point Cloud Segmentation
Point cloud segmentation is a fundamental task in 3D.
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
Graphics
Compositionally Generalizable 3D Structure Prediction
However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.
Point-Based Multi-View Stereo Network
More specifically, our method predicts the depth in a coarse-to-fine manner.
