36 citations · 81 across the 12 of their papers we have counts for
12 papers
Tracking Everything in Robotic-Assisted Surgery
Bohan Zhan, Wang Zhao, Yi Fang +5
Accurate tracking of tissues and instruments in videos is crucial for Robotic-Assisted Minimally Invasive Surgery (RAMIS), as it enables the robot to comprehend the surgical scene…
TBench: Benchmarking Current Progress in Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +6
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3…
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
Yuze He, Peng Wang, Yubin Hu +4
This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI), for high-quality view synthesis of complex scenes. MPI with Normalized De…
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
Sheng Ye, Yubin Hu, Matthieu Lin +4
The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. R…
O-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
Yubin Hu, Sheng Ye, Wang Zhao +5
Occlusion is a common issue in 3D reconstruction from RGB-D videos, often blocking the complete reconstruction of objects and presenting an ongoing problem. In this paper, we propo…
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild
Wang Zhao, Shaohui Liu, Hengkai Guo +2
Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performanc…
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Wang Zhao, Shaohui Liu, Yi Wei +2
In this paper, we introduce a deep multi-view stereo (MVS) system that jointly predicts depths, surface normals and per-view confidence maps. The key to our approach is a novel sol…
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
Yi Wei, Shaohui Liu, Yongming Rao +3
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radianc…
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
Wang Zhao, Shaohui Liu, Yezhi Shu +1
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth an…
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
Yiheng Han, Wang Zhao, Jia Pan +3
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recen…
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction
Yi Wei, Shaohui Liu, Wang Zhao +2
In this paper, we present a new perspective towards image-based shape generation. Most existing deep learning based shape reconstruction methods employ a single-view deterministic…
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Li Yi, Wang Zhao, He Wang +2
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal a…