mlsanityData to 14 Oct 2026

DC3DO: Diffusion Classifier for 3D Objects

arXiv 2408.06693 ↗First posted 13 Aug 2024cs.CV cs.AI cs.CGcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from these models, our approach, the Diffusion Classifier for 3D Objects (DC3DO), enables zero-shot classification of 3D shapes without additional training. On average, our method achieves a 12.5 percent improvement compared to its multiview counterparts, demonstrating superior multimodal reasoning over discriminative approaches. DC3DO employs a class-conditional diffusion model trained on ShapeNet, and we run inferences on point clouds of chairs and cars. This work highlights the potential of generative models in 3D object classification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 45 of its 53 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

45 of 53 references.

Closest ancestors · 10

Same direction, newest first

  1. 2023DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior11 Dec
  2. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion14 Nov
  3. 2023Text-to-3D with Classifier Score Distillation30 Oct
  4. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization29 Jun
  5. 2023DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification25 May
  6. 2023Zero-1-to-3: Zero-shot One Image to 3D Object20 Mar
  7. 20223D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models1 Dec
  8. 2022LION: Latent Point Diffusion Models for 3D Shape Generation12 Oct
  9. 2022DreamFusion: Text-to-3D using 2D Diffusion29 Sep
  10. 2021Diffusion Probabilistic Models for 3D Point Cloud Generation2 Mar

Other references · 35

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  5. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  6. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distributioncited by 745
  7. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  8. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  9. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
  10. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  11. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  12. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
23 more
  1. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  2. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  3. 2021Diffusion Autoencoders: Toward a Meaningful and Decodable Representationcited by 449
  4. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  5. 2023PointLLM: Empowering Large Language Models to Understand Point Cloudscited by 333
  6. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 306
  7. 2019On the "steerability" of generative adversarial networkscited by 248
  8. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 202
  9. 2023Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Followingcited by 194
  10. 2022Concrete Score Matching: Generalized Score Matching for Discrete Datacited by 148
  11. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  12. 2023Locally Attentional SDF Diffusion for Controllable 3D Shape Generationcited by 130
  13. 2017MarrNet: 3D Shape Reconstruction via 2.5D Sketchescited by 108
  14. 2023Compositional 3D Scene Generation using Locally Conditioned Diffusioncited by 101
  15. 2020Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classificationcited by 57
  16. 20233DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4Vcited by 15
  17. 2023Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generationcited by 14
  18. 2021Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecturecited by 7
  19. 2022Gaussian Blue Noisecited by 6
  20. 2021IFCNet: A Benchmark Dataset for IFC Entity Classificationcited by 4
  21. 2024PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognitioncited by 3
  22. 2023Multi-task 3D building understanding with multi-modal pretrainingcited by 1
  23. 2023JM3D & JM3D-LLM: Elevating 3D Understanding with Joint Multi-modal Cuescited by 1

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  3. 2023Generating Images with 3D Annotations Using Diffusion Models
  4. 2023Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data
  5. 20233DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
  6. 2023Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
  7. 2025PointDico: Contrastive 3D Representation Learning Guided by Diffusion Models
  8. 2023Diffusion-based 3D Object Detection with Random Boxes