Point Cloud Generation

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 59 papers with code (117 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
  28 Jun 2019 12 repositories listed Syntology ran 2 of 33 samples · 31 unverified
  Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
• 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
  15 May 2019 4 repositories listed Syntology ran 4 of 25 samples · 21 unverified
  In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN.
• Adversarial Autoencoders for Compact Representations of 3D Point Clouds
  19 Nov 2018 4 repositories listed
  Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
• Diffusion Probabilistic Models for 3D Point Cloud Generation
  2 Mar 2021 3 repositories listed Syntology ran 22 of 29 samples · 7 unverified · 2 pointer-only (licence)
  We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
• Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
  21 Jun 2017 3 repositories listed
  Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
• Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
  12 Aug 2024 2 repositories listed
  Our fastest variant outperforms all non-diffusion generative approaches on unconditional shape generation, the most popular benchmark for evaluating point cloud generative models, while our largest model achieves…
• TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process
  1 Jan 2024 2 repositories listed
  However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images.
• LION: Latent Point Diffusion Models for 3D Shape Generation
  12 Oct 2022 2 repositories listed
  To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii)…
• SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
  29 Mar 2021 2 repositories listed Syntology ran 17 of 27 samples · 10 unverified
  Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.
• Conditional Invertible Flow for Point Cloud Generation
  16 Oct 2019 2 repositories listed
  This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.
• TopoDiT-3D: Topology-Aware Diffusion Transformer with Bottleneck Structure for 3D Point Cloud Generation
  14 May 2025 1 repository listed
  To address this limitation, we propose TopoDiT-3D, a Topology-Aware Diffusion Transformer with a bottleneck structure for 3D point cloud generation.
• EAGLE: Contextual Point Cloud Generation via Adaptive Continuous Normalizing Flow with Self-Attention
  5 Mar 2025 1 repository listed
  We also propose adaptive continuous normalizing flows by introducing adaptive bias correction mechanism.
• Generative diffusion model with inverse renormalization group flows
  15 Jan 2025 1 repository listed
  Here we introduce a renormalization group-based diffusion model that leverages multiscale nature of data distributions for realizing a high-quality data generation.
• ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction
  25 Dec 2024 1 repository listed
  The learned multimodal features are fed into a transformer-based decoder for high-resolution point cloud reconstruction.
• PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose Clothing
  6 Nov 2024 1 repository listed
  We motivate our work by noting that most methods require a parametric model of the human body to ground pose-dependent deformations.
• Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
  5 Jun 2024 1 repository listed
  Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
• ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds
  3 Jun 2024 1 repository listed
  Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment.
• L-MAGIC: Language Model Assisted Generation of Images with Coherence
  3 Jun 2024 1 repository listed Syntology ran 11 of 11 samples · 0 unverified
  However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.
• Learning to Discretize Denoising Diffusion ODEs
  24 May 2024 1 repository listed Syntology ran 14 of 15 samples · 1 unverified · 15 pointer-only (licence)
  We achieve FIDs of 2.
• Taming Transformers for Realistic Lidar Point Cloud Generation
  8 Apr 2024 1 repository listed
  Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling.
• LidarDM: Generative LiDAR Simulation in a Generated World
  3 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos.
• RangeLDM: Fast Realistic LiDAR Point Cloud Generation
  15 Mar 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge.
• General Point Model Pretraining with Autoencoding and Autoregressive
  1 Jan 2024 1 repository listed
  Compared to models like Point-BERT MaskPoint and PointMAE our GPM achieves superior performance in point cloud understanding tasks.
• Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection
  19 Dec 2023 1 repository listed
  Based on this process, we introduce SGAS, a model for part editing that employs two strategies: feature disentanglement and constraint.
• LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
  17 Sep 2023 1 repository listed Syntology ran 8 of 9 samples · 1 unverified
  In this work, we present R2DM, a novel generative model for LiDAR data that can generate diverse and high-fidelity 3D scene point clouds based on the image representation of range and reflectance intensity.
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion
  31 Jul 2023 1 repository listed
  Radar is ubiquitous in autonomous driving systems due to its low cost and good adaptability to bad weather.
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach
  22 Jul 2023 1 repository listed
  A generative model for high-fidelity point clouds is of great importance in synthesizing 3d environments for applications such as autonomous driving and robotics.
• DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
  4 Jul 2023 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 1 pointer-only (licence)
  Recent Diffusion Transformers (e.
• Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
  12 Jun 2023 1 repository listed
  Volume-DROID takes camera images (monocular or stereo) or frames from a video as input and combines DROID-SLAM, point cloud registration, an off-the-shelf semantic segmentation network, and Convolutional Bayesian Kernel…
• NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
  28 Apr 2023 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  We verify the effectiveness of our NeRF-LiDAR by training different 3D segmentation models on the generated LiDAR point clouds.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.