40 citations · 99 across the 28 of their papers we have counts for
28 papers
PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet
Xiaopei Wu, Chenshu Hou, Liang Peng +9
3D dense captioning, an emerging vision-language task, aims to generate descriptive sentences for each object in the 3D scene. Despite the impressive results achieved by previous m…
MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs
Xianglong He, Junyi Chen, Di Huang +5
In the domain of 3D content creation, achieving optimal mesh topology through AI models has long been a pursuit for 3D artists. Previous methods, such as MeshGPT, have explored the…
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
Di Zhang, Jianbo Wu, Jingdi Lei +9
This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework…
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild
Weicai Ye, Xinyu Chen, Ruohao Zhan +7
This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild. Traditional framewor…
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models
Wentao Qu, Jing Wang, YongShun Gong +2
Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex g…
DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion
Weicai Ye, Chenhao Ji, Zheng Chen +7
Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even images remains constr…
Semi-supervised 3D Object Detection with PatchTeacher and PillarMix
Xiaopei Wu, Liang Peng, Liang Xie +6
Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to g…
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
Xiaopei Wu, Yuenan Hou, Xiaoshui Huang +8
Training deep models for LiDAR semantic segmentation is challenging due to the inherent sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity p…
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
Di Zhang, Xiaoshui Huang, Dongzhan Zhou +2
This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative integration of Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS), designed to enhance perf…
Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Qikai Wang, Rundong He, Yongshun Gong +4
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often…
3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset
Junjie Zhang, Tianci Hu, Xiaoshui Huang +2
Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive ass…
Taming Stable Diffusion for Text to 360° Panorama Image Generation
Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella +4
Generative models, e.g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts. Yet, the generation of 360-degree panorama images from text remains…
A Comprehensive Survey on 3D Content Generation
Jian Liu, Xiaoshui Huang, Tianyu Huang +8
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is…
GVGEN: Text-to-3D Generation with Volumetric Representation
Xianglong He, Junyi Chen, Sida Peng +6
In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. To addr…
NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection
Chenxi Huang, Yuenan Hou, Weicai Ye +5
NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by innovatively utilizing NeRF to enhance representation learning. Despite its notable performance, w…
Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models
Dingning Liu, Xiaoshui Huang, Yuenan Hou +5
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point clo…
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
Dingning Liu, Xiaomeng Dong, Renrui Zhang +5
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals…
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
Wentao Qu, Yuantian Shao, Lingwu Meng +2
Point cloud upsampling (PCU) enriches the representation of raw point clouds, significantly improving the performance in downstream tasks such as classification and reconstruction.…
UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation
Zexiang Liu, Yangguang Li, Youtian Lin +7
Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D…
Point Cloud Pre-training with Diffusion Models
Xiao Zheng, Xiaoshui Huang, Guofeng Mei +5
Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However, due to the unordered and non-uniform…
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Yongqi Huang, Peng Ye, Xiaoshui Huang +4
Structural re-parameterization is a general training scheme for Convolutional Neural Networks (CNNs), which achieves performance improvement without increasing inference cost. As V…
UniG3D: A Unified 3D Object Generation Dataset
Qinghong Sun, Yangguang Li, ZeXiang Liu +5
The field of generative AI has a transformative impact on various areas, including virtual reality, autonomous driving, the metaverse, gaming, and robotics. Among these application…
LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark
Zhenfei Yin, Jiong Wang, Jianjian Cao +9
Large language models have emerged as a promising approach towards achieving general-purpose AI agents. The thriving open-source LLM community has greatly accelerated the developme…
3D Point Cloud Pre-training with Knowledge Distillation from 2D Images
Yuan Yao, Yuanhan Zhang, Zhenfei Yin +3
The recent success of pre-trained 2D vision models is mostly attributable to learning from large-scale datasets. However, compared with 2D image datasets, the current pre-training…