We have witnessed significant progress in deep learning-based 3D vision,
ranging from neural radiance field (NeRF) based 3D representation learning to
applications in novel view synthesis (NVS). However, existing scene-level
datasets for deep learning-based 3D vision, limited to either synthetic
environments or a narrow selection of real-world scenes, are quite
insufficient. This insufficiency not only hinders a comprehensive benchmark of
existing methods but also caps what could be explored in deep learning-based 3D
analysis. To address this critical gap, we present DL3DV-10K, a large-scale
scene dataset, featuring 51.2 million frames from 10,510 videos captured from
65 types of point-of-interest (POI) locations, covering both bounded and
unbounded scenes, with different levels of reflection, transparency, and
lighting. We conducted a comprehensive benchmark of recent NVS methods on
DL3DV-10K, which revealed valuable insights for future research in NVS. In
addition, we have obtained encouraging results in a pilot study to learn
generalizable NeRF from DL3DV-10K, which manifests the necessity of a
large-scale scene-level dataset to forge a path toward a foundation model for
learning 3D representation. Our DL3DV-10K dataset, benchmark results, and
models will be publicly accessible at https://dl3dv-10k.github.io/DL3DV-10K/.
Main Themes:
- The lack of comprehensive scene-level datasets for deep learning-based 3D vision
- The limitations of existing datasets in benchmarking methods and exploring deep learning-based 3D analysis
- The introduction of DL3DV-10K, a large-scale scene dataset with 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest locations
- A comprehensive benchmark of recent NVS (novel view synthesis) methods on DL3DV-10K, providing valuable insights for future research in NVS
- Pilot study results showing the potential of DL3DV-10K for learning generalizable NeRF (neural radiance field)
- The public accessibility of DL3DV-10K dataset, benchmark results, and models at https://dl3dv-10k.github.io/DL3DV-10K/
Recommendations:
- Invest in the development of comprehensive scene-level datasets: The article highlights the insufficiency of existing datasets in deep learning-based 3D vision. To promote innovation and strategic foresight, industry players should invest in the creation of large-scale, diverse datasets that capture a wide range of real-world scenes.
- Encourage collaboration and open access: The DL3DV-10K dataset, benchmark results, and models are made publicly accessible. This fosters collaboration among researchers and enables the industry to build upon existing work. Encouraging open access to datasets and research findings can accelerate progress in the field.
- Continuously evaluate and benchmark methods: The comprehensive benchmark of recent NVS methods on DL3DV-10K provided valuable insights for future research. To drive innovation, it is important to continuously evaluate and benchmark methods, encouraging researchers and practitioners to push the boundaries of deep learning-based 3D analysis.
- Explore applications beyond novel view synthesis: While the article focuses on the benchmarking of NVS methods, there is an opportunity to explore other applications of deep learning-based 3D vision. Industry players should encourage research and experimentation in areas such as object recognition, scene reconstruction, and virtual reality.
- Promote research in generalizable models: The pilot study showed promising results in learning generalizable NeRF from the DL3DV-10K dataset. Investing in research that aims to develop foundation models for learning 3D representation can have a transformative impact on the field.
By implementing these recommendations, the industry can foster innovation and strategic foresight in deep learning-based 3D vision, leading to advancements in various applications and pushing the boundaries of what can be achieved in this field.
References:
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible at https://dl3dv-10k.github.io/DL3DV-10K/.