Skip to main content

SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15122))

Included in the following conference series:

  • 310 Accesses

Abstract

Open-world 3D generation has recently attracted considerable attention. While many single-image-to-3D methods have yielded visually appealing outcomes, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users’ expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a novel method, SpaRP, to reconstruct a 3D textured mesh and estimate the relative camera poses for these sparse-view images. SpaRP distills knowledge from 2D diffusion models and finetunes them to implicitly deduce the 3D spatial relationships between the sparse views. The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy but also exhibits strong efficiency. It requires only about 20 s to produce a textured mesh and camera poses for the input views.

C. Xu, L. Chen, R. Shi and M. Liu—This work is done while the author is an intern at Hillbot Inc.

H. Su and M. Liu—Equal advisory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)

    Article  Google Scholar 

  2. Bradski, G.: Perspective-n-Point (PnP) pose computation (the openCV library) (2000). https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html

  3. Chan, E.R., et al.: GeNVS: generative novel view synthesis with 3D-aware diffusion models (2023)

    Google Scholar 

  4. Chen, A., et al.: MVSNeRF: fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14124–14133 (2021)

    Google Scholar 

  5. Chen, R., Chen, Y., Jiao, N., Jia, K.: Fantasia3D: disentangling geometry and appearance for high-quality text-to-3D content creation. arXiv preprint arXiv:2303.13873 (2023)

  6. Chen, Z., Wang, F., Liu, H.: Text-to-3D using gaussian splatting. arXiv preprint arXiv:2309.16585 (2023)

  7. Collins, J., et al.: ABO: dataset and benchmarks for real-world 3D object understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21126–21136 (2022)

    Google Scholar 

  8. Deitke, M., et al.: Objaverse-XL: a universe of 10m+ 3D objects. arXiv preprint arXiv:2307.05663 (2023)

  9. Deitke, M., et al.: Objaverse: a universe of annotated 3D objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13142–13153 (2023)

    Google Scholar 

  10. Deng, C., et al.: NeRDi: single-view nerf synthesis with language-guided diffusion as general image priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20637–20647 (2023)

    Google Scholar 

  11. Denninger, M., et al.: BlenderProc. arXiv preprint arXiv:1911.01911 (2019)

  12. Downs, L., et al.: Google scanned objects: a high-quality dataset of 3D scanned household items. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 2553–2560. IEEE (2022)

    Google Scholar 

  13. Guo, Y.C., et al.: threestudio: a unified framework for 3D content generation (2023). https://github.com/threestudio-project/threestudio

  14. Hong, Y., et al.: LRM: large reconstruction model for single image to 3D. arXiv preprint arXiv:2311.04400 (2023)

  15. Jain, A., Mildenhall, B., Barron, J.T., Abbeel, P., Poole, B.: Zero-shot text-guided object generation with dream fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 867–876 (2022)

    Google Scholar 

  16. Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5885–5894 (2021)

    Google Scholar 

  17. Jiang, H., Jiang, Z., Grauman, K., Zhu, Y.: Few-view object reconstruction with unknown categories and camera poses. arXiv preprint arXiv:2212.04492 (2022)

  18. Jiang, H., Jiang, Z., Zhao, Y., Huang, Q.: Leap: liberate sparse-view 3D modeling from camera poses. arXiv preprint arXiv:2310.01410 (2023)

  19. Johari, M.M., Lepoittevin, Y., Fleuret, F.: GeoNeRF: generalizing nerf with geometry priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18365–18375 (2022)

    Google Scholar 

  20. Jun, H., Nichol, A.: Shap-E: generating conditional 3D implicit functions. arXiv preprint arXiv:2305.02463 (2023)

  21. Karnewar, A., Vedaldi, A., Novotny, D., Mitra, N.J.: HOLODIFFUSION: training a 3D diffusion model using 2D images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18423–18433 (2023)

    Google Scholar 

  22. Kim, M., Seo, S., Han, B.: InfoNeRF: ray entropy minimization for few-shot neural volume rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12912–12921 (2022)

    Google Scholar 

  23. Kong, X., Liu, S., Lyu, X., Taher, M., Qi, X., Davison, A.J.: EscherNet: a generative model for scalable view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9503–9513 (2024)

    Google Scholar 

  24. Kulhánek, J., Derner, E., Sattler, T., Babuška, R.: ViewFormer: Nerf-free neural rendering from few images using transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13675, pp. 198–216. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19784-0_12

    Chapter  Google Scholar 

  25. Lai, Z., Liu, S., Efros, A.A., Wang, X.: Video autoencoder: self-supervised disentanglement of static 3d structure and motion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9730–9740 (2021)

    Google Scholar 

  26. Laine, S., Hellsten, J., Karras, T., Seol, Y., Lehtinen, J., Aila, T.: Modular primitives for high-performance differentiable rendering. ACM Trans. Graph. 39(6), 1–14 (2020)

    Article  Google Scholar 

  27. Lee, H.H., Chang, A.X.: Understanding pure clip guidance for voxel grid nerf models. arXiv preprint arXiv:2209.15172 (2022)

  28. Li, J., et al.: Instant3D: fast text-to-3D with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214 (2023)

  29. Lin, A., Zhang, J.Y., Ramanan, D., Tulsiani, S.: RelPose++: recovering 6D poses from sparse-view observations. arXiv preprint arXiv:2305.04926 (2023)

  30. Lin, C.H., et al.: Magic3D: high-resolution text-to-3D content creation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 300–309 (2023)

    Google Scholar 

  31. Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: BARF: bundle-adjusting neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5741–5751 (2021)

    Google Scholar 

  32. Liu, M., et al.: One-2-3-45++: fast single image to 3D objects with consistent multi-view generation and 3D diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10072–10083 (2024)

    Google Scholar 

  33. Liu, M., et al.: OpenShape: scaling up 3D shape representation towards open-world understanding. Adv. Neural Inf. Process. Syst. 36 (2024)

    Google Scholar 

  34. Liu, M., et al.: One-2-3-45: any single image to 3D mesh in 45 seconds without per-shape optimization. Adv. Neural Inf. Process. Syst. 36 (2024)

    Google Scholar 

  35. Liu, R., Wu, R., Van Hoorick, B., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: zero-shot one image to 3D object. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9298–9309 (2023)

    Google Scholar 

  36. Liu, Y., et al.: SyncDreamer: generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453 (2023)

  37. Liu, Y., et al.: Neural rays for occlusion-aware image-based rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7824–7833 (2022)

    Google Scholar 

  38. Long, X., et al.: Wonder3D: single image to 3D using cross-domain diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9970–9980 (2024)

    Google Scholar 

  39. Long, X., Lin, C., Wang, P., Komura, T., Wang, W.: SparseNeuS: fast generalizable neural surface reconstruction from sparse views. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 210–227. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_13

    Chapter  Google Scholar 

  40. Melas-Kyriazi, L., Laina, I., Rupprecht, C., Vedaldi, A.: RealFusion: 360deg reconstruction of any object from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8446–8455 (2023)

    Google Scholar 

  41. Metzer, G., Richardson, E., Patashnik, O., Giryes, R., Cohen-Or, D.: Latent-NeRF for shape-guided generation of 3D shapes and textures. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12663–12673 (2023)

    Google Scholar 

  42. Michel, O., Bar-On, R., Liu, R., Benaim, S., Hanocka, R.: Text2Mesh: text-driven neural stylization for meshes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13492–13502 (2022)

    Google Scholar 

  43. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99–106 (2021)

    Article  Google Scholar 

  44. Mohammad Khalid, N., Xie, T., Belilovsky, E., Popa, T.: Clip-mesh: generating textured meshes from text using pretrained image-text models. In: SIGGRAPH Asia 2022 Conference Papers, pp. 1–8 (2022)

    Google Scholar 

  45. Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5480–5490 (2022)

    Google Scholar 

  46. Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: DreamFusion: text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988 (2022)

  47. Qian, G., et al.: Magic123: one image to high-quality 3D object generation using both 2D and 3D diffusion priors. arXiv preprint arXiv:2306.17843 (2023)

  48. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  49. Raj, A., et al.: DreamBooth3D: subject-driven text-to-3D generation. arXiv preprint arXiv:2303.13508 (2023)

  50. Ramesh, A., et al.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821–8831. PMLR (2021)

    Google Scholar 

  51. Rematas, K., Martin-Brualla, R., Ferrari, V.: ShaRF: shape-conditioned radiance fields from a single view. arXiv preprint arXiv:2102.08860 (2021)

  52. Ren, Y., Zhang, T., Pollefeys, M., Süsstrunk, S., Wang, F.: VolRecon: volume rendering of signed ray distance functions for generalizable multi-view reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16685–16695 (2023)

    Google Scholar 

  53. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)

    Google Scholar 

  54. Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural. Inf. Process. Syst. 35, 36479–36494 (2022)

    Google Scholar 

  55. Sajjadi, M.S., et al.: Scene representation transformer: geometry-free novel view synthesis through set-latent scene representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6229–6238 (2022)

    Google Scholar 

  56. Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)

    Google Scholar 

  57. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part III. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  58. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  59. Seo, J., et al.: Let 2D diffusion model know 3D-consistency for robust text-to-3D generation. arXiv preprint arXiv:2303.07937 (2023)

  60. Shi, R., et al.: Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110 (2023)

  61. Shi, R., Wei, X., Wang, C., Su, H.: ZeroRF: fast sparse view \(360^{\circ }\) reconstruction with zero pretraining. arXiv preprint arXiv:2312.09249 (2023)

  62. Shi, Y., et al.: MVDream: multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512 (2023)

  63. Sinha, S., Zhang, J.Y., Tagliasacchi, A., Gilitschenski, I., Lindell, D.B.: SparsePose: sparse-view camera pose regression and refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21349–21359 (2023)

    Google Scholar 

  64. Stereopsis, R.M.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)

    Article  Google Scholar 

  65. Tang, J., Chen, Z., Chen, X., Wang, T., Zeng, G., Liu, Z.: LGM: large multi-view gaussian model for high-resolution 3D content creation. arXiv preprint arXiv:2402.05054 (2024)

  66. Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: DreamGaussian: generative Gaussian splatting for efficient 3D content creation. arXiv preprint arXiv:2309.16653 (2023)

  67. Tang, J., et al.: Make-it-3D: high-fidelity 3D creation from a single image with diffusion prior. arXiv preprint arXiv:2303.14184 (2023)

  68. Tewari, A., et al.: Diffusion with forward models: solving stochastic inverse problems without direct supervision. arXiv preprint arXiv:2306.11719 (2023)

  69. Tochilkin, D., et al.: TripoSR: fast 3D object reconstruction from a single image (2024)

    Google Scholar 

  70. Trevithick, A., Yang, B.: GRF: learning a general radiance field for 3D representation and rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15182–15192 (2021)

    Google Scholar 

  71. Truong, P., Rakotosaona, M.J., Manhardt, F., Tombari, F.: SPARF: neural radiance fields from sparse and noisy poses. In: CVF Conference on Computer Vision and Pattern Recognition, CVPR, vol. 1 (2023)

    Google Scholar 

  72. Tung, H.Y.F., Cheng, R., Fragkiadaki, K.: Learning spatial common sense with geometry-aware recurrent networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2595–2603 (2019)

    Google Scholar 

  73. Wang, H., Du, X., Li, J., Yeh, R.A., Shakhnarovich, G.: Score Jacobian chaining: lifting pretrained 2D diffusion models for 3D generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12619–12629 (2023)

    Google Scholar 

  74. Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6d object pose and size estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2642–2651 (2019)

    Google Scholar 

  75. Wang, J., Rupprecht, C., Novotny, D.: PoseDiffusion: solving pose estimation via diffusion-aided bundle adjustment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9773–9783 (2023)

    Google Scholar 

  76. Wang, P., et al.: Is attention all NeRF needs? arXiv preprint arXiv:2207.13298 (2022)

  77. Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689 (2021)

  78. Wang, P., et al.: PF-LRM: pose-free large reconstruction model for joint pose and shape prediction. arXiv preprint arXiv:2311.12024 (2023)

  79. Wang, Q., et al.: IBRNet: learning multi-view image-based rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2021)

    Google Scholar 

  80. Wang, Z., et al.: ProlificDreamer: high-fidelity and diverse text-to-3D generation with variational score distillation. arXiv preprint arXiv:2305.16213 (2023)

  81. Wang, Z., Wu, S., Xie, W., Chen, M., Prisacariu, V.A.: NeRF–: neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064 (2021)

  82. Weng, H., et al.: Consistent123: improve consistency for one image to 3D object synthesis. arXiv preprint arXiv:2310.08092 (2023)

  83. Wu, C.H., Chen, Y.C., Solarte, B., Yuan, L., Sun, M.: iFusion: inverting diffusion for pose-free reconstruction from sparse views (2023)

    Google Scholar 

  84. Wu, R., et al.: ReconFusion: 3D reconstruction with diffusion priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21551–21561 (2024)

    Google Scholar 

  85. Wu, T., et al.: OmniObject3D: large-vocabulary 3D object dataset for realistic perception, reconstruction and generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 803–814 (2023)

    Google Scholar 

  86. Xia, Y., Tang, H., Timofte, R., Van Gool, L.: SiNeRF: sinusoidal neural radiance fields for joint pose estimation and scene reconstruction. arXiv preprint arXiv:2210.04553 (2022)

  87. Xu, D., Jiang, Y., Wang, P., Fan, Z., Wang, Y., Wang, Z.: NeuralLift-360: lifting an in-the-wild 2D photo to a 3D object with 360deg views. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4479–4489 (2023)

    Google Scholar 

  88. Xu, J., et al.: Dream3D: zero-shot text-to-3D synthesis using 3D shape prior and text-to-image diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20908–20918 (2023)

    Google Scholar 

  89. Yang, H., et al.: ContraNeRF: generalizable neural radiance fields for synthetic-to-real novel view synthesis via contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16508–16517 (2023)

    Google Scholar 

  90. Yang, Z., Ren, Z., Bautista, M.A., Zhang, Z., Shan, Q., Huang, Q.: FvOR: robust joint shape and pose optimization for few-view object reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2497–2507 (2022)

    Google Scholar 

  91. Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Adv. Neural. Inf. Process. Syst. 34, 4805–4815 (2021)

    Google Scholar 

  92. Ye, J., Wang, P., Li, K., Shi, Y., Wang, H.: Consistent-1-to-3: consistent image to 3D view synthesis via geometry-aware diffusion models. arXiv preprint arXiv:2310.03020 (2023)

  93. Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: neural radiance fields from one or few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4578–4587 (2021)

    Google Scholar 

  94. Yu, C., Zhou, Q., Li, J., Zhang, Z., Wang, Z., Wang, F.: Points-to-3D: bridging the gap between sparse points and shape-controllable text-to-3D generation. arXiv preprint arXiv:2307.13908 (2023)

  95. Zhang, J.Y., Ramanan, D., Tulsiani, S.: RelPose: predicting probabilistic relative rotation for single objects in the wild. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. Lecture Notes in Computer Science, vol. 13691, pp. 592–611. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_34

    Chapter  Google Scholar 

  96. Zhang, L.: Reference-only control (2023). https://github.com/Mikubill/sd-webui-controlnet/discussions/1236

  97. Zhou, Z., Tulsiani, S.: SparseFusion: distilling view-conditioned diffusion for 3D reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12588–12597 (2023)

    Google Scholar 

Download references

Acknowledgements

We thank Chong Zeng, Xinyue Wei for the discussion and help with data processing, and Peng Wang for providing the evaluation set. We also extend our thanks to all annotators for their meticulous annotations.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minghua Liu .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 8184 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, C. et al. (2025). SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15122. Springer, Cham. https://doi.org/10.1007/978-3-031-73039-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73039-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73038-2

  • Online ISBN: 978-3-031-73039-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics