217 citations · 307 across the 36 of their papers we have counts for
24 papers · 1 filter
CycleDiff: Cycle Diffusion Models for Unpaired Image-to-image Translation
Shilong Zou, Yuhang Huang, Renjiao Yi +2
We introduce a diffusion-based cross-domain image translator in the absence of paired training data. Unlike GAN-based methods, our approach integrates diffusion models to learn the…
Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction
Zhirui Gao, Renjiao Yi, Yaqiao Dai +4
This paper presents an end-to-end framework for reconstructing 3D parametric curves directly from multi-view edge maps. Contrasting with existing two-stage methods that follow a se…
RemixFusion: Residual-based Mixed Representation for Large-scale Online RGB-D Reconstruction
Yuqing Lan, Chenyang Zhu, Shuaifeng Zhi +5
The introduction of the neural implicit representation has notably propelled the advancement of online dense reconstruction techniques. Compared to traditional explicit representat…
BoxFusion: Reconstruction-Free Open-Vocabulary 3D Object Detection via Real-Time Multi-View Box Fusion
Yuqing Lan, Chenyang Zhu, Zhirui Gao +5
Open-vocabulary 3D object detection has gained significant interest due to its critical applications in autonomous driving and embodied AI. Existing detection methods, whether offl…
OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging
Yijie Tang, Jiazhao Zhang, Yuqing Lan +4
Online zero-shot 3D instance segmentation of a progressively reconstructed scene is both a critical and challenging task for embodied applications. With the success of visual found…
VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis
Zhifeng Wang, Renjiao Yi, Xin Wen +2
Angiography imaging is a medical imaging technique that enhances the visibility of blood vessels within the body by using contrast agents. Angiographic images can effectively assis…
CAD-NeRF: Learning NeRFs from Uncalibrated Few-view Images by CAD Model Retrieval
Xin Wen, Xuening Zhu, Renjiao Yi +3
Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of n…
Generic Objects as Pose Probes for Few-shot View Synthesis
Zhirui Gao, Renjiao Yi, Chenyang Zhu +3
Radiance fields including NeRFs and 3D Gaussians demonstrate great potential in high-fidelity rendering and scene reconstruction, while they require a substantial number of posed i…
Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadrics
Zhirui Gao, Renjiao Yi, Yuhang Huang +3
Low-level 3D representations, such as point clouds, meshes, NeRFs and 3D Gaussians, are commonly used for modeling 3D objects and scenes. However, cognitive studies indicate that h…
Relighting Scenes with Object Insertions in Neural Radiance Fields
Xuening Zhu, Renjiao Yi, Xin Wen +2
The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR). Previous methods focused on inserting virtual objects using CAD m…
F2M-Reg: Unsupervised RGB-D Point Cloud Registration with Frame-to-Model Optimization
Zhinan Yu, Zheng Qin, Yijie Tang +4
This work studies the problem of unsupervised RGB-D point cloud registration, which aims at training a robust registration model without ground-truth pose supervision. Existing met…
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds
Minhao Li, Zheng Qin, Zhirui Gao +4
The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature d…
SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction
Hui Tian, Chenyang Zhu, Yifei Shi +1
Learning-based surface reconstruction based on unsigned distance functions (UDF) has many advantages such as handling open surfaces. We propose SuperUDF, a self-supervised UDF lear…
Weakly-supervised Single-view Image Relighting
Renjiao Yi, Chenyang Zhu, Kai Xu
We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scen…
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images
Yunfan Ye, Renjiao Yi, Zhirui Gao +3
We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images. To do so, we learn a neural implicit field representing the densit…
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement
Zhirui Gao, Renjiao Yi, Zheng Qin +3
Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in manufacturing industry for estimating the poses of differ…
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition
Yaqiao Dai, Renjiao Yi, Chenyang Zhu +2
Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually l…
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
Chenyi Liu, Fei Chen, Lu Deng +5
The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well…
DisARM: Displacement Aware Relation Module for 3D Detection
Yao Duan, Chenyang Zhu, Yuqing Lan +3
We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea of…
ARM3D: Attention-based relation module for indoor 3D object detection
Yuqing Lan, Yao Duan, Chenyi Liu +4
Relation context has been proved to be useful for many challenging vision tasks. In the field of 3D object detection, previous methods have been taking the advantage of context enc…
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng +1
Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstructio…
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +3
We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-insta…
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Fenggen Yu, Kun Liu, Yan Zhang +2
Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly l…
Faces as Lighting Probes via Unsupervised Deep Highlight Extraction
Renjiao Yi, Chenyang Zhu, Ping Tan +1
We present a method for estimating detailed scene illumination using human faces in a single image. In contrast to previous works that estimate lighting in terms of low-order basis…