Welcome to light fields

Overbeck, Ryan S.; Erickson, Daniel; Evangelakos, Daniel; Debevec, Paul

doi:10.1145/3226552.3226557

Cited by 14 publications

(21 citation statements)

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“…Finally, (3) while of high quality, offers only content consisting of head rotations and small translations (3DoF+). The photorealism is close or higher to solution (2) [32].…”

Section: Related Workmentioning

confidence: 76%

RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR

Bonatto¹,

Fachada²,

Lafruit³

2020

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“…Finally, (3) while of high quality, offers only content consisting of head rotations and small translations (3DoF+). The photorealism is close or higher to solution (2) [32].…”

Section: Related Workmentioning

confidence: 76%

RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR

Bonatto¹,

Fachada²,

Lafruit³

2020

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“…On an LF dataset (Lego Bulldozer) from the Stanford LF archive with complex details in the scene and with a high resolution of 1536 × 1152 Overbeck et al [2018] reports a compression ratio of 178:1 for PSNR of 45 dB. On the same LF dataset, our method achieves a compression of a ratio 60:1 for a PSNR of 42 dB.…”

Section: Evaluation and Analysismentioning

confidence: 90%

RLFC

Pratapa

Manocha

2019

Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Figure 1: Our method (RLFC) is based on computing a hierarchy of new images: Representative Key Views (RKV) which capture the redundancies of the LF in the top levels of the hierarchy and Sparse Residual Views (SRV) that capture specific details of the LF in the bottom levels of the hierarchy. We present a visualization of the images (RKV & SRV) computed in RLFC hierarchy on a small sample grid of the LF for two datasets. RLFC achieves compression by removing the insignificant details and exploiting the sparsity of the SRV images in the hierarchy. ABSTRACTWe present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 − 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 − 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512 × 512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations. 1

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“…The data sizes of the sampled LF vary from hundreds of MB [Levoy and Hanrahan 1996] to hundreds of GB [Levoy et al 2000;Lin and Shum 2000] depending on the scene complexity, sampling rate, and sampling resolution. For 360°p anoramic light fields [Overbeck et al 2018] the LF data-sizes are close to 4-6 GBs. Therefore, compressing the LF is necessary for storing, transmitting, and interactive rendering.…”

Section: Introductionmentioning

confidence: 93%

“…Also, using motion compensation vectors for parallax correction to reduce artifacts during LF rendering may be possible. Our current implementation focuses on 4D two-plane parameterization of the light fields; in the future, we would like to extend our compression approach to more complex parameterizations such as spherical [Ihm et al 1997], panoramic [Overbeck et al 2018], and unstructured LF [Davis et al 2012]. Our current implementation for encoding LFI is single threaded and slow.…”

Section: Conclusion Limitations and Future Workmentioning

confidence: 99%

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

Pratapa

Manocha

2019

Computer Graphics Forum

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We present a new motion‐compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and the hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run‐time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two‐plane parameterized light fields and obtain a compression ratio of 30–800× with a PSNR of 40–45 dB. Overall, we observe a ∼2–5× improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512 × 512 on an NVIDIA GTX‐980 at ∼200 fps.

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Welcome to light fields

Cited by 14 publications

References 0 publications

RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR

RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR

RLFC

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

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