Fetching the paper…
Reading the bibliography…
LiDAR scene synthesis is an emerging solution to scarcity in 3D data for robotic tasks such as autonomous driving.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
M. Gschwandtner, R. Kwitt, A. Uhl, and W. Pree, “BlenSor: Blender sensor simulation toolbox,” in
2011
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,”
2014
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves,
2016
Earlier work this paper cites.
X. Yue, B. Wu, S. A. Seshia, K. Keutzer, and A. L. Sangiovanni-Vincentelli, “A lidar point cloud generator: from a virtual world to autonomous driving,” in
2018
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in
2019
Earlier work this paper cites.
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation,” in
2019
Earlier work this paper cites.
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” in
2019
Earlier work this paper cites.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” in
2019
Earlier work this paper cites.
C. Choy, J. Gwak, and S. Savarese, “4D spatio-temporal ConvNets: Minkowski convolutional neural networks,” in
2019
Earlier work this paper cites.
J. Fang, D. Zhou, F. Yan, T. Zhao, F. Zhang, Y. Ma, L. Wang, and R. Yang, “Augmented lidar simulator for autonomous driving,” in
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” in
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in
2020
Earlier work this paper cites.
L. T. Triess, D. Peter, C. B. Rist, and J. M. Zöllner, “Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study,” in
2020
Earlier work this paper cites.
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in
2020
Earlier work this paper cites.
J. Fang, X. Zuo, D. Zhou, S. Jin, S. Wang, and L. Zhang, “LiDAR-Aug: A general rendering-based augmentation framework for 3D object detection,” in
2021
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in
2021
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in
2021
Cited alongside, same era.
V. Zyrianov, X. Zhu, and S. Wang, “Learning to generate realistic lidar point clouds,” in
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” in
2022
Cited alongside, same era.
Y. Liao, J. Xie, and A. Geiger, “Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,”
2022
Cited alongside, same era.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman,
2022
Cited alongside, same era.
H. Ran, V. Guizilini, and Y. Wang, “Towards Realistic Scene Generation with LiDAR Diffusion Models,” in
2024
Later among the works it cites.
G. Puy, S. Gidaris, A. Boulch, O. Siméoni, C. Sautier, P. Pérez, A. Bursuc, and R. Marlet, “Three pillars improving vision foundation model distillation for lidar,” in
2024
Later among the works it cites.
N. Ma, M. Goldstein, M. S. Albergo, N. M. Boffi, E. Vanden-Eijnden, and S. Xie, “Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers,” in
2024
Later among the works it cites.
S. S. Puligilla, M. Omama, H. Zaidi, U. S. Parihar, and M. Krishna, “LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization,” in
2024
Later among the works it cites.
S. Yu, S. Kwak, H. Jang, J. Jeong, J. Huang, J. Shin, and S. Xie, “Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
A. Ando, S. Gidaris, A. Bursuc, G. Puy, A. Boulch, and R. Marlet, “Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,” in
2023
Cited alongside, same era.
Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” in
2023
Cited alongside, same era.
S. Peng, K. Genova, C. M. Jiang, A. Tagliasacchi, M. Pollefeys, and T. Funkhouser, “OpenScene: 3D scene understanding with open vocabularies,” in
2023
Cited alongside, same era.
2023
Cited alongside, same era.
D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach, “SDXL: Improving latent diffusion models for high-resolution image synthesis,” in
2024
Cited alongside, same era.
P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel,
2024
Cited alongside, same era.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
J. Yao, B. Yang, and X. Wang, “Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models,” in
2025
Later among the works it cites.
T. Martyniuk, G. Puy, A. Boulch, R. Marlet, and R. de Charette, “Lidpm: Rethinking point diffusion for lidar scene completion,” in
2025
Later among the works it cites.
K. Nakashima, X. Liu, T. Miyawaki, Y. Iwashita, and R. Kurazume, “Fast lidar data generation with rectified flows,” in
2025
Later among the works it cites.
T. Yan, J. Yin, X. Lang, R. Yang, C.-Z. Xu, and J. Shen, “Olidm: Object-aware lidar diffusion models for autonomous driving,” in
2025
Later among the works it cites.
E. Kirby, M. Chen, R. Marlet, and N. Samet, “Logen: Toward lidar object generation by point diffusion,”
2025
Later among the works it cites.
A. Buburuzan, A. Sharma, J. Redford, P. K. Dokania, and R. Mueller, “Mobi: Multimodal object inpainting using diffusion models,” in
2025
Later among the works it cites.
D. Zhu, Y. Hu, Y. Liu, D. Lu, L. Kong, and S. Ilic, “Spiral: Semantic-aware progressive lidar scene generation and understanding,” in
2025
Later among the works it cites.
2025
Later among the works it cites.
X. Wu, D. DeTone, D. Frost, T. Shen, C. Xie, N. Yang, J. Engel, R. Newcombe, H. Zhao, and J. Straub, “Sonata: Self-supervised learning of reliable point representations,” in
2025
Later among the works it cites.