Neural Scene Representation & Rendering: Introduction to Novel View Synthesis
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The document discusses recent advances in novel view synthesis using neural rendering. It describes different approaches for representing 3D scenes like voxel grids, multi-plane images, and implicit functions. Voxel-based methods can render high quality novel views but are memory intensive. Implicit functions enable more compact representations but rendering is slow. Hybrid implicit/explicit and image-based methods provide faster rendering but cannot represent scenes globally. The document outlines open challenges in reducing rendering costs, improving generalization, and enabling new applications in scene understanding.
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Neural Scene Representation & Rendering: Introduction to Novel View Synthesis
- 1. Vincent Sitzmann, SIGGRAPH 2021 Novel View Synthesis for Objects and Scenes Neural Rerendering in the Wild, Meshry et al. 2019 Scene Representation Networks, Sitzmann et al. 2019 Neural Volumes, Lombardi et al. 2019 Deep View, Flynn et al. 2019
- 2. Vincent Sitzmann, SIGGRAPH 2021 Goal: Render novel views given sparse set of observations + + Observations Image + Pose & Intrinsics { , , … { Model Novel Views
- 3. Vincent Sitzmann, SIGGRAPH 2021 Training on dataset of images Differentiab le Renderer Scene Representati on Image Loss Reconstructi on Scene Representation + Differentiable Renderer: Train on images + , + , … Observations Re-Rendered Observations , , … , , …
- 4. Vincent Sitzmann, SIGGRAPH 2021 How to do few-shot reconstruction? Differentiab le Renderer Scene Representati on Image Loss Scene Representation + Differentiable Renderer: Train on images Prior-based Reconstruction: If method learns prior, enables few-shot reconstruction! Single Observation + ? Prior-Based Reconstructi on Re-Rendered Observations , , … , , …
- 5. Vincent Sitzmann, SIGGRAPH 2021 Overview Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Hybrid Implicit/Explicit Volumetric Image-based Rasterization Both Scene Representation and Differentiable Renderer often adapted from traditional computer graphics.
- 6. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons Hybrid Implicit/Explicit Volumetric Image-based Rasterization
- 7. Vincent Sitzmann, SIGGRAPH 2021 Voxel-based methods Lombardi et al., SIGGRAPH 2019 Sitzmann et al., CVPR 2018 DeepVoxels Neural Volumes HoloGAN Phuoc et al., ICCV 2019
- 8. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization
- 9. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization
- 10. Vincent Sitzmann, SIGGRAPH 2021 Neural Implicit Approaches Scene Representation Networks Generalizes across scenes Sitzmann et al., NeurIPS 2019 NeRF Single-scene Mildenhall et al., ECCV 2020 Implicit Differentiable Renderer Single-scene Yariv et al., NeurIPS 2020 Volumetric • Higher Quality • Easy convergence • Very expensive Near Far Sphere tracing • Faster • Fewer network evaluations • Convergence more difficult Differentiable Volumetric Rendering Generalizes across scenes Niemeyer et al., CVPR 2020
- 11. Vincent Sitzmann, SIGGRAPH 2021 Dynamic Extensions Nerfies, Park et al., arXiv 2019 D-NeRF, Pumarola et al. 2020 Neural Radiance Flow, Du et al., arXiv 2020 Neural Scene Flow Fields, Li et al., CVPR 2021 Space-time Neural Irradiance Fields, Xian et al., arXiv 2020
- 12. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering
- 13. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering
- 14. Vincent Sitzmann, SIGGRAPH 2021 Hybrid Implicit / Explicit PiFU, Saito et al., ICCV 2019 GRF, Trevithick et al., arXiv 2020 pixelNeRF, Yu et. al., CVPR 2021 MVSNerf, Chen et al., arXiv 2021 Learn local (image patch-based) priors Neural Sparse Voxel Fields, Liu et. al., NeurIPS 2020 Unconstrained Scene Generation with Locally Conditioned Radiance Fields, DeVries et al., arXiv 2021
- 15. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering Significant Speedup Admits local priors No compact representation No global priors
- 16. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering Significant Speedup Admits local priors No compact representation No global priors
- 17. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering Significant Speedup Admits local priors No compact representation No global priors High-quality Fast Large Size Only 2.5D
- 18. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization True 3D High quality Compact Admits global priors Extremely expensive, slow rendering Significant Speedup Admits local priors High-quality Fast Large Size Only 2.5D No compact representation No global priors
- 19. Vincent Sitzmann, SIGGRAPH 2021 Image-based methods Stable View Synthesis Riegler et al., CVPR 2021 IBRNet, Wang et al., CVPR 2021
- 20. Vincent Sitzmann, SIGGRAPH 2021 Requirements Scene Representati on Multi-Plane Images Voxelgrids Implicit Function Renderer (Alpha) compositing Volumetric Ray-based Sphere-Tracing Volumetric Pros Cons “True 3D” High quality No reconstruction priors Memory O(n3) Hybrid Implicit/Explicit Volumetric Image-based Rasterization / Volumetric True 3D High quality Compact Admits global priors Extremely expensive, slow rendering Significant Speedup Admits local priors No compact representation No global priors Memory O(n3) High-quality Fast Large Size Only 2.5D High-quality Fast Not compact: Needs source images.
- 21. Vincent Sitzmann, SIGGRAPH 2021 Summary: Open Challenges Expensive Rendering • Rendering requires hundreds of samples per ray – at train and test time. • How to do non-Lambertian effects? Multi-bounce barely tractable. Generalization • Local conditioning enables stronger generalization, but doesn’t learn object-/scene-centric representations. Can we have both? Scene Understanding • Lots of important applications outside of computer graphics worth exploring!