Fetching the paper…
Reading the bibliography…
Diffusion models have shown remarkable results in generating 2D images and small-scale 3D objects.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR (2012)
2012
Earlier work this paper cites.
2015
Earlier work this paper cites.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: MICCAI (2016)
2016
Earlier work this paper cites.
Sra, M., Garrido-Jurado, S., Schmandt, C., Maes, P.: Procedurally generated virtual reality from 3d reconstructed physical space. In: Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology (2016)
2016
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Graham, B., Engelcke, M., Van Der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: CVPR (2018)
2018
Earlier work this paper cites.
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: A dataset for semantic scene understanding of lidar sequences. In: ICCV (2019)
2019
Earlier work this paper cites.
Lan, Z., Yew, Z.J., Lee, G.H.: Robust point cloud based reconstruction of large-scale outdoor scenes. In: CVPR (2019)
2019
Earlier work this paper cites.
Ögün, M.N., Kurul, R., Yaşar, M.F., Turkoglu, S.A., Avci, Ş., Yildiz, N.: Effect of leap motion-based 3d immersive virtual reality usage on upper extremity function in ischemic stroke patients. Arquivos de neuro-psiquiatria (2019)
2019
Earlier work this paper cites.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: CVPR (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Li, X., Li, C., Tong, Z., Lim, A., Yuan, J., Wu, Y., Tang, J., Huang, R.: Campus3d: A photogrammetry point cloud benchmark for hierarchical understanding of outdoor scene. In: ACM MM (2020)
2020
Earlier work this paper cites.
Li, Y., Ma, L., Zhong, Z., Liu, F., Chapman, M.A., Cao, D., Li, J.: Deep learning for lidar point clouds in autonomous driving: A review. NeurIPS (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Austin, J., Johnson, D.D., Ho, J., Tarlow, D., Van Den Berg, R.: Structured denoising diffusion models in discrete state-spaces. In: NeurIPS (2021)
2021
Earlier work this paper cites.
Cheng, A.C., Li, X., Sun, M., Yang, M.H., Liu, S.: Learning 3d dense correspondence via canonical point autoencoder. In: NeurIPS (2021)
2021
Cited alongside, same era.
Cong, Y., Chen, R., Ma, B., Liu, H., Hou, D., Yang, C.: A comprehensive study of 3-d vision-based robot manipulation. IEEE Transactions on Cybernetics (2021)
2021
Cited alongside, same era.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. In: NeurIPS (2021)
2021
Cited alongside, same era.
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: CVPR (2021)
2021
Cited alongside, same era.
Ma, Q., Yang, J., Tang, S., Black, M.J.: The power of points for modeling humans in clothing. In: ICCV (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
Later among the works it cites.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. In: NeurIPS (2022)
2022
Later among the works it cites.
Wilson, J., Song, J., Fu, Y., Zhang, A., Capodieci, A., Jayakumar, P., Barton, K., Ghaffari, M.: Motionsc: Data set and network for real-time semantic mapping in dynamic environments. IEEE Robotics and Automation Letters 7
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mascaro, R., Teixeira, L., Chli, M.: Diffuser: Multi-view 2d-to-3d label diffusion for semantic scene segmentation. In: ICRA (2021)
2021
Cited alongside, same era.
Moro, S., Komuro, T.: Generation of virtual reality environment based on 3d scanned indoor physical space. In: ISVC (2021)
2021
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: ICML (2021)
2021
Cited alongside, same era.
Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: ICCV (2021)
2021
Cited alongside, same era.
Anvekar, T., Tabib, R.A., Hegde, D., Mudengudi, U.: Vg-vae: A venatus geometry point-cloud variational auto-encoder. In: CVPR (2022)
2022
Cited alongside, same era.
Cheng, A.C., Li, X., Liu, S., Sun, M., Yang, M.H.: Learning 3d dense correspondence via canonical point autoencoder. In: ECCV (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Wu, P., Jia, X., Chen, L., Yan, J., Li, H., Qiao, Y.: Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline. In: NeurIPS (2022)
2022
Later among the works it cites.
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K.: Lion: Latent point diffusion models for 3d shape generation. In: NeurIPS (2022)
2022
Later among the works it cites.
Chen, Z., Wang, G., Liu, Z.: Scenedreamer: Unbounded 3d scene generation from 2d image collections. In: arXiv preprint arXiv: 2302.01330 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Su, S.Y., Bagautdinov, T., Rhodin, H.: Npc: Neural point characters from video. In: ICCV (2023)
2023
Closest in time.
2023
Closest in time.
Tang, Y., He, H., Wang, Y., Mao, Z., Wang, H.: Multi-modality 3d object detection in autonomous driving: A review. Neurocomputing (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Zheng, Y., Yifan, W., Wetzstein, G., Black, M.J., Hilliges, O.: Pointavatar: Deformable point-based head avatars from videos. In: CVPR (2023)
2023
Closest in time.