Fetching the paper…
Reading the bibliography…
Event cameras offer promising advantages such as high dynamic range and low latency, making them well-suited for challenging lighting conditions and fast-moving scenarios.
Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Earlier work this paper cites.
Semi-dense 3d reconstruction with a stereo event camera
Zhou, Y., Gallego, G., Rebecq, H., Kneip, L., Li, H., and Scaramuzza, D · 2018
Earlier work this paper cites.
Neural volumes: Learning dynamic renderable volumes from images
Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., and Sheikh, Y · 2019
Earlier work this paper cites.
High speed and high dynamic range video with an event camera
Rebecq, H., Ranftl, R., Koltun, V., and Scaramuzza, D · 2019
Earlier work this paper cites.
Deepvoxels: Learning persistent 3d feature embeddings
Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., and Zollhofer, M · 2019
Earlier work this paper cites.
Visionblender: a tool to efficiently generate computer vision datasets for robotic surgery
Cartucho, J., Tukra, S., Li, Y., S. Elson, D., and Giannarou, S · 2020
Earlier work this paper cites.
Learning monocular dense depth from events
Hidalgo-Carrió, J., Gehrig, D., and Scaramuzza, D · 2020
Earlier work this paper cites.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J., Ramamoorthi, R., and Ng, R · 2020
Earlier work this paper cites.
Fast image reconstruction with an event camera
Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R., and Scaramuzza, D · 2020
Earlier work this paper cites.
Event enhanced high-quality image recovery
Wang, B., He, J., Yu, L., Xia, G.-S., and Yang, W · 2020
Earlier work this paper cites.
Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., and Srinivasan, P. P · 2021
Earlier work this paper cites.
Spade-e2vid: Spatially-adaptive denormalization for event-based video reconstruction
Cadena, P. R. G., Qian, Y., Wang, C., and Yang, M · 2021
Earlier work this paper cites.
Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., and Su, H · 2021
Earlier work this paper cites.
v2e: From video frames to realistic dvs events
Hu, Y., Liu, S.-C., and Delbruck, T · 2021
Cited alongside, same era.
Dynamic plane convolutional occupancy networks
Lionar, S., Emtsev, D., Svilarkovic, D., and Peng, S · 2021
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2021
Cited alongside, same era.
Back to event basics: Self-supervised learning of image reconstruction for event cameras via photometric constancy
Paredes-Vallés, F. and de Croon, G. C · 2021
Cited alongside, same era.
Ibrnet: Learning multi-view image-based rendering
Wang, Q., Wang, Z., Genova, K., Srinivasan, P. P., Zhou, H., Barron, J. T., Martin-Brualla, R., Snavely, N., and Funkhouser, T · 2021
Cited alongside, same era.
Event-based video reconstruction using transformer
Weng, W., Zhang, Y., and Xiong, Z · 2021
Spiking neural networks for frame-based and event-based single object localization
Barchid, S., Mennesson, J., Eshraghian, J., Djéraba, C., and Bennamoun, M · 2023
Later among the works it cites.
Learning to estimate two dense depths from lidar and event data
Brebion, V., Moreau, J., and Davoine, F · 2023
Later among the works it cites.
Hexplane: A fast representation for dynamic scenes
Cao, A. and Johnson, J · 2023
Later among the works it cites.
Neurbf: A neural fields representation with adaptive radial basis functions
Chen, Z., Li, Z., Song, L., Chen, L., Yu, J., Yuan, J., and Xu, Y · 2023
Later among the works it cites.
Ge, W., Hu, T., Zhao, H., Liu, S., and Chen, Y.-C · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Mip-nerf 360: Unbounded anti-aliased neural radiance fields, 2022
Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., and Hedman, P · 2022
Cited alongside, same era.
Tensorf: Tensorial radiance fields
Chen, A., Xu, Z., Geiger, A., Yu, J., and Su, H · 2022
Cited alongside, same era.
Plenoxels: Radiance fields without neural networks
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., and Kanazawa, A · 2022
Cited alongside, same era.
Efficient neural radiance fields for interactive free-viewpoint video
Lin, H., Peng, S., Xu, Z., Yan, Y., Shuai, Q., Bao, H., and Zhou, X · 2022
Cited alongside, same era.
Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
Sun, C., Sun, M., and Chen, H.-T · 2022
Cited alongside, same era.
Point-nerf: Point-based neural radiance fields
Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., and Neumann, U · 2022
Cited alongside, same era.
Huang, D., Peng, S., He, T., Yang, H., Zhou, X., and Ouyang, W · 2023
Later among the works it cites.
Ev-nerf: Event based neural radiance field
Hwang, I., Kim, J., and Kim, Y. M · 2023
Later among the works it cites.
Stereo depth estimation based on adaptive stacks from event cameras
Jianguo, Z., Pengfei, W., Sunan, H., Cheng, X., and Rodney, T. S. H · 2023
Later among the works it cites.
3d gaussian splatting for real-time radiance field rendering
Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G · 2023
Later among the works it cites.
E-nerf: Neural radiance fields from a moving event camera
Klenk, S., Koestler, L., Scaramuzza, D., and Cremers, D · 2023
Later among the works it cites.
Sensing diversity and sparsity models for event generation and video reconstruction from events
Liu, S. and Dragotti, P. L · 2023
Later among the works it cites.
Eventnerf: Neural radiance fields from a single colour event camera
Rudnev, V., Elgharib, M., Theobalt, C., and Golyanik, V · 2023
Later among the works it cites.
Learning to generate and manipulate 3d radiance field by a hierarchical diffusion framework with clip latent
Wang, J., Zhang, Z., and Xu, R · 2023
Later among the works it cites.
Dist: Rendering deep implicit signed distance function with differentiable sphere tracing
Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., and Cui, Z · 2028
Closest in time.