Papers Point Cloud Generation
“Point Cloud Generation” 태그가 달린 논문 145편 · 필터 해제
Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation
Point clouds are an important carrier of three-dimensional spatial information, and their quality directly affects the performance of downstream perception tasks such as object detection and tracking. However, millimeter…
Point Cloud GenerationObject DetectionPoint Clouds3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases
This comprehensive review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications. The analysis covers both traditional approaches and eme…
Point Cloud Generation3D ReconstructionPoint-MF: One-step Point Cloud Generation from a Single Image via Mean Flows
Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based reconstructors achieve high accuracy, they typically require many denoisin…
Point Cloud GenerationPoint CloudsFUSE-Flow: Scalable Real-Time Multi-View Point Cloud Reconstruction Using Confidence
Real-time multi-view point cloud reconstruction is a core problem in 3D vision and immersive perception, with wide applications in VR, AR, robotic navigation, digital twins, and computer interaction. Despite advances in …
Point Cloud GenerationPoint CloudsQuartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance
We introduce the Quartet of Diffusions, a structure-aware point cloud generation framework that explicitly models part composition and symmetry. Unlike prior methods that treat shape generation as a holistic process or o…
Point Cloud GenerationClass-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation
Most 3D scene generation methods are limited to only generating object bounding box parameters while newer diffusion methods also generate class labels and latent features. Using object size or latent feature, they then …
Point Cloud GenerationScene GenerationRANGER: A Monocular Zero-Shot Semantic Navigation Framework through Visual Contextual Adaptation
Efficient target localization and autonomous navigation in complex environments are fundamental to real-world embodied applications. While recent advances in multimodal foundation models have enabled zero-shot object goa…
Point Cloud Generation3D ReconstructionLiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs
Generating realistic and diverse LiDAR point clouds is crucial for autonomous driving simulation. Although previous methods achieve LiDAR point cloud generation from user inputs, they struggle to attain high-quality resu…
Point Cloud GenerationAutonomous DrivingPoint CloudsSymmetry Matters: Auditing and Symmetrizing 3D Generative Models
Symmetry is a strong prior present in many object categories, yet standard benchmarks for 3D generative models rarely report whether this prior is preserved. We study symmetry preservation in unconditional point cloud ge…
Point Cloud GenerationRadarGen: Automotive Radar Point Cloud Generation from Cameras
We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar m…
Point Cloud GenerationPoint Clouds4D-RaDiff: Latent Diffusion for 4D Radar Point Cloud Generation
Automotive radar has shown promising developments in environment perception due to its cost-effectiveness and robustness in adverse weather conditions. However, the limited availability of annotated radar data poses a si…
Point Cloud GenerationData AugmentationObject DetectionPoint CloudsGAOT: Generating Articulated Objects Through Text-Guided Diffusion Models
Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between textual descriptions and 3D articulated…
Point Cloud GenerationMFM-point: Multi-scale Flow Matching for Point Cloud Generation
In recent years, point cloud generation has gained significant attention in 3D generative modeling. Among existing approaches, point-based methods directly generate point clouds without relying on other representations s…
Point Cloud GenerationPoint CloudsRange-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing
Training autonomous driving and navigation systems requires large and diverse point cloud datasets that capture complex edge case scenarios from various dynamic urban settings. Acquiring such diverse scenarios from real-…
Point Cloud GenerationAutonomous DrivingPoint CloudsDriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving
The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant impr…
Point Cloud GenerationAutonomous DrivingScene GenerationPoint CloudsRadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving
Accurate 3D scene motion perception significantly enhances the safety and reliability of an autonomous driving system. Benefiting from its all-weather operational capability and unique perceptual properties, 4D mmWave ra…
Point Cloud GenerationAutonomous VehiclesAutonomous DrivingUMIGen: A Unified Framework for Egocentric Point Cloud Generation and Cross-Embodiment Robotic Imitation Learning
Data-driven robotic learning faces an obvious dilemma: robust policies demand large-scale, high-quality demonstration data, yet collecting such data remains a major challenge owing to high operational costs, dependence o…
Point Cloud GenerationRethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation
As 3D point clouds become a cornerstone of modern technology, the need for sophisticated generative models and reliable evaluation metrics has grown exponentially. In this work, we first expose that some commonly used me…
Point Cloud GenerationPoint CloudsPointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction
Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered…
Point Cloud GenerationStrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformer
In the field of 3D point cloud generation, numerous 3D generative models have demonstrated the ability to generate diverse and realistic 3D shapes. However, the majority of these approaches struggle to generate controlla…
Point Cloud GenerationPoint Clouds