OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds

• URL: http://arxiv.org/abs/2512.08506v1
• Date: Tue, 09 Dec 2025 11:47:57 GMT
• Title: OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds
• Authors: Jialu Sui, Rui Liu, Hongsheng Zhang,
• Abstract summary: OCCDiff is proposed to flexibly gather high-quality 3D profiles of the building in diverse resolution.<n>Our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy data.
• Score: 3.962264364723744
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A major challenge in reconstructing buildings from LiDAR point clouds lies in accurately capturing building surfaces under varying point densities and noise interference. To flexibly gather high-quality 3D profiles of the building in diverse resolution, we propose OCCDiff applying latent diffusion in the occupancy function space. Our OCCDiff combines a latent diffusion process with a function autoencoder architecture to generate continuous occupancy functions evaluable at arbitrary locations. Moreover, a point encoder is proposed to provide condition features to diffusion learning, constraint the final occupancy prediction for occupancy decoder, and insert multi-modal features for latent generation to latent encoder. To further enhance the model performance, a multi-task training strategy is employed, ensuring that the point encoder learns diverse and robust feature representations. Empirical results show that our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy data.

Related papers

• DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation [47.409626500688866]
  We present the DINO Spherical Autoencoder (DINO-SAE), a framework that bridges semantic representation and pixel-level reconstruction.<n>Our approach achieves state-of-the-art reconstruction quality, reaching 0.37 rFID and 26.2 dB PSNR, while maintaining strong semantic alignment to the pretrained VFM.
  arXiv  Detail & Related papers  (2026-01-30T12:25:34Z)
• LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detection [5.820124526272312]
  Existing LiDAR-Camera fusion methods have achieved strong results in 3D object detection.<n>We propose LDRFusion, a novel Lidar-dominant two-stage refinement framework for multi-sensor fusion.<n>Our framework consistently achieves strong performance across multiple categories and difficulty levels.
  arXiv  Detail & Related papers  (2025-07-22T04:35:52Z)
• KDC-Diff: A Latent-Aware Diffusion Model with Knowledge Retention for Memory-Efficient Image Generation [2.0250638970950905]
  KDC-Diff is a novel and scalable generative framework designed to significantly reduce computational overhead while maintaining high performance.<n>Our model demonstrates strong performance across FID, CLIP, KID, and LPIPS metrics while achieving substantial reductions in parameter count, inference time, and FLOPs.
  arXiv  Detail & Related papers  (2025-05-11T14:40:51Z)
• Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation [49.202383675543466]
  We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images.<n>To derive high-quality reconstructions through few-step inferences, we emphasize the critical issue of regularizing the learning of score function in states of random noise.
  arXiv  Detail & Related papers  (2025-03-20T09:18:10Z)
• CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection [15.63287331447003]
  Collaborative 3D object detection holds significant importance in the field of autonomous driving.<n>Due to pose estimation errors and time delays, the fusion of information across agents often results in feature representations with spatial and temporal noise.<n>We propose CoDiff, a novel robust collaborative perception framework.
  arXiv  Detail & Related papers  (2025-02-17T03:20:52Z)
• Unpaired Deblurring via Decoupled Diffusion Model [55.21345354747609]
  We propose UID-Diff, a generative-diffusion-based model designed to enhance deblurring performance on unknown domains.<n>We employ two Q-Formers as structural features and blur patterns extractors separately. The features extracted will be used for the supervised deblurring task on synthetic data and the unsupervised blur-transfer task.<n>Experiments on real-world datasets demonstrate that UID-Diff outperforms existing state-of-the-art methods in blur removal and structural preservation.
  arXiv  Detail & Related papers  (2025-02-03T17:00:40Z)
• High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity [69.32473738284374]
  Diffusion models have revolutionized text-to-image synthesis by delivering exceptional quality, fine detail resolution, and strong contextual awareness.<n>We propose DiffDIS, a diffusion-driven segmentation model that taps into the potential of the pre-trained U-Net within diffusion models.<n>Experiments on the DIS5K dataset demonstrate the superiority of DiffDIS, achieving state-of-the-art results through a streamlined inference process.
  arXiv  Detail & Related papers  (2024-10-14T02:49:23Z)
• Diffusion-based 3D Object Detection with Random Boxes [58.43022365393569]
  Existing anchor-based 3D detection methods rely on empiricals setting of anchors, which makes the algorithms lack elegance. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. In the inference stage, the model progressively refines a set of random boxes to the prediction results.
  arXiv  Detail & Related papers  (2023-09-05T08:49:53Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.