Search Results for author: Hao Pan

Found 25 papers, 10 papers with code

ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models

no code implementations22 May 2024 Rui Xu, Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Shiqing Xin, Changhe Tu, Taku Komura, Wenping Wang

We show that the space spanned by the combination of dimensions and attributes is insufficiently sampled by existing training scheme of diffusion generative models, causing degraded test time performance.

MVD$^2$: Efficient Multiview 3D Reconstruction for Multiview Diffusion

no code implementations22 Feb 2024 Xin-Yang Zheng, Hao Pan, Yu-Xiao Guo, Xin Tong, Yang Liu

By finetuning pretrained large image diffusion models with 3D data, the MVD methods first generate multiple views of a 3D object based on an image or text prompt and then reconstruct 3D shapes with multiview 3D reconstruction.

3D Generation 3D Reconstruction

Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating

no code implementations21 Feb 2024 Yifan Yanggong, Hao Pan, Lei Wang

Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research.

Decision Making

StructRe: Rewriting for Structured Shape Modeling

no code implementations29 Nov 2023 Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Taku Komura, Wenping Wang

Such a localized rewriting process enables probabilistic modeling of ambiguous structures and robust generalization across object categories.

Object

CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs

no code implementations CVPR 2024 Haocheng Yuan, Jing Xu, Hao Pan, Adrien Bousseau, Niloy J. Mitra, Changjian Li

CAD programs are a popular way to compactly encode shapes as a sequence of operations that are easy to parametrically modify.

iPUNet:Iterative Cross Field Guided Point Cloud Upsampling

1 code implementation13 Oct 2023 Guangshun Wei, Hao Pan, Shaojie Zhuang, Yuanfeng Zhou, Changjian Li

To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration.

point cloud upsampling

Large Language Models Empowered Autonomous Edge AI for Connected Intelligence

no code implementations6 Jul 2023 Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng Li, Jun Zhang, Khaled B. Letaief

The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.

Code Generation Federated Learning +3

Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

2 code implementations14 Apr 2023 Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong, Yang Liu, Hao Pan, Peng-Shuai Wang, Xin Tong, Baining Guo

We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset.

Ranked #2 on 3D Object Detection on S3DIS (using extra training data)

3D Object Detection Scene Understanding +1

3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining

no code implementations14 Apr 2023 Siming Yan, YuQi Yang, YuXiao Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, QiXing Huang

Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision.

Decoder

Discovering Design Concepts for CAD Sketches

1 code implementation26 Oct 2022 Yuezhi Yang, Hao Pan

Sketch design concepts are recurring patterns found in parametric CAD sketches.

Implicit Conversion of Manifold B-Rep Solids by Neural Halfspace Representation

1 code implementation21 Sep 2022 Hao-Xiang Guo, Yang Liu, Hao Pan, Baining Guo

We present a novel implicit representation -- neural halfspace representation (NH-Rep), to convert manifold B-Rep solids to implicit representations.

Surface Reconstruction

ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation

1 code implementation29 May 2022 Haoxiang Guo, Shilin Liu, Hao Pan, Yang Liu, Xin Tong, Baining Guo

We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i. e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved.

CAD Reconstruction Decoder

Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch

no code implementations23 Jan 2022 Zhi Deng, Yang Liu, Hao Pan, Wassim Jabi, Juyong Zhang, Bailin Deng

In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes.

Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving

no code implementations4 Nov 2021 Daofei Li, Ao Liu, Hao Pan, Wentao Chen

By investigating the causes of 4, 300 video clips of traffic accidents, we find that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents, especially in those involving trucks.

Decision Making

DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation

1 code implementation27 Jul 2021 Yilin Wen, Xiangyu Li, Hao Pan, Lei Yang, Zheng Wang, Taku Komura, Wenping Wang

Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects.

6D Pose Estimation Metric Learning +2

Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective

no code implementations19 Apr 2021 Lei Chu, Hao Pan, Wenping Wang

We present a novel approach for completing and reconstructing 3D shapes from incomplete scanned data by using deep neural networks.

Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

1 code implementation CVPR 2021 Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu

We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces.

3D Object Reconstruction 3D Reconstruction +1

Supercongruences for central trinomial coefficients

no code implementations9 Dec 2020 Hao Pan, Zhi-Wei Sun

For each $n=0, 1, 2,\ldots$ the central trinomial coefficient $T_n$ is the coefficient of $x^n$ in the expansion of $(x^2+x+1)^n$.

Number Theory Combinatorics

PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

1 code implementation CVPR 2020 Yu-Qi Yang, Shilin Liu, Hao Pan, Yang Liu, Xin Tong

Surface meshes are widely used shape representations and capture finer geometry data than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their non-Euclidean structure.

Scene Segmentation Segmentation

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