IP Library › Granted Patent US 12,586,295
Granted Patent B2
US 12,586,295 · App. 18/393,004 · Granted Mar 24, 2026

Apparatus and method for generating new viewpoint image using few-shot 2D images

Inventors: Junghyun Cho (Seoul, KR); Ig Jae Kim (Seoul, KR); Seok Yeong Lee (Seoul, KR); Jun Yong Choi (Seoul, KR)
Assignee: Korea Institute of Science and Technology
G06T15/205G06T7/30G06T7/90G06T15/506G06T2207/10024G06T2207/20081G06T2207/20084
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,586,295
App. No.
18/393,004
Granted
Mar 24, 2026
Kind
B2
Abstract

The present disclosure relates to an apparatus and a method for generating an image, which may generate a new viewpoint image using few-shot 2D images and is characterized by extracting and learning albedo representing intrinsic color information from the few-show 2D input images, intrinsically decomposing a color value synthesized in the form of patch from a new viewpoint using the few-shot 2D input images and extracting patch-wise albedo during learning, perform geometric alignment necessary for pixel correspondence between a new viewpoint image and a selected 2D input image using a depth value synthesized in the form of patch from the new viewpoint, and generating the new viewpoint image by calculating an albedo consistency loss between the patch-wise albedo.

Claims (18)

1 . An apparatus for generating a new viewpoint image using few-shot 2D images, comprising:

a full-image intrinsic decomposition unit configured to receive the few-shot 2D images, intrinsically decompose each input image, and extract albedo representing intrinsic color information from each 2D input image;

a patch image intrinsic decomposition unit configured to learn using the albedo provided from the full-image intrinsic decomposition unit, and learn to minimize an albedo consistency loss, intrinsically decompose a color value synthesized in the form of patch from a new viewpoint using the few-shot 2D input images during learning, and extract patch-wise albedo;

an alignment unit configured to perform geometric alignment necessary for pixel correspondence between a new viewpoint image and a 2D input image selected from the few-shot 2D input images using a depth value synthesized in the form of patch from the new viewpoint, calculate an albedo consistency loss between the albedo extracted by the full-image intrinsic decomposition unit and the patch-wise albedo extracted by the patch image intrinsic decomposition unit based on the pixel correspondence, and provide the albedo consistency loss to the patch image intrinsic decomposition unit; and

an image generation unit configured to generate the new viewpoint image from the few-shot 2D input images by reflecting the patch-wise albedo extracted by the patch image intrinsic decomposition unit finishing learning.

2 . The apparatus of claim 1 , wherein the full-image intrinsic decomposition unit intrinsically decomposes each of the 2D input images offline using a given global context.

3 . The apparatus of claim 1 , wherein the alignment unit performs geometric alignment by performing projective transformation based on the depth value synthesized in the form of patch from the new viewpoint.

4 . The apparatus of claim 1 , wherein the alignment unit minimizes inaccurate pixel correspondence by reflecting a depth consistency loss of the selected 2D input image.

5 . The apparatus of claim 1 , wherein the albedo consistency loss is calculated by reflecting a weight term that minimizes a projection error.

6 . A method of generating a new viewpoint image using few-shot 2D images, comprising:

a full-image intrinsic decomposing operation of receiving the few-shot 2D images, intrinsically decomposing each input image, and extracting albedo representing intrinsic color information from each 2D input image;

a patch image intrinsic decomposing operation of learning using the albedo extracted in the full-image intrinsic decomposing operation, and learning to minimize an albedo consistency loss, intrinsically decomposing a color value synthesized in the form of patch from a new viewpoint using the few-shot 2D input images during learning, and extracting patch-wise albedo;

an aligning operation of performing geometric alignment necessary for pixel correspondence between a new viewpoint image and a 2D input image selected from the few-shot 2D input images using a depth value synthesized in the form of patch from the new viewpoint, calculating an albedo consistency loss between the albedo extracted in the full-image intrinsic decomposing operation and the patch-wise albedo extracted in the patch image intrinsic decomposing operation based on the pixel correspondence, and provide the albedo consistency loss to the patch image intrinsic decomposing operation; and

an image generating operation of generating the new viewpoint image from the few-shot 2D input images by intrinsically decomposing the color value synthesized in the form of patch from the new viewpoint using the few-shot 2D input images and reflecting the extracted patch-wise albedo, after the learning is finished.

7 . The method of claim 6 , wherein in the full-image intrinsic decomposing operation, each of the 2D input images is intrinsically decomposed offline using a given global context.

8 . The method of claim 6 , wherein in the aligning operation, geometric alignment is performed by performing projective transformation based on the depth value synthesized in the form of patch from the new viewpoint.

9 . The method of claim 6 , wherein in the aligning operation, inaccurate pixel correspondence is minimized by reflecting a depth consistency loss of the selected 2D input image.

10 . The method of claim 6 , wherein the albedo consistency loss is calculated by reflecting a weight term that minimizes a projection error.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 22, 2023
From: CHO, JUNGHYUN; KIM, IG JAE; LEE, SEOK YEONG; CHOI, JUN YONG
To: KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY
Reel/Frame 065939/0449 →
Priority Claims (1)
KR 10-2023-0157935 · Nov 15, 2023 · national
Continuity (1)
Related Publication 20250157137A1 · May 15, 2025
References Cited (14)
US 10950037B2 · Sunkavalli et al. · 2021 [cited by applicant]
US 20180293711A1 · Vogels · 2018 [cited by examiner]
US 20200160593A1 · Gu · 2020 [cited by examiner]
US 20210012576A1 · Riegler et al. · 2021 [cited by applicant]
US 20220130111A1 · Martin Brualla · 2022 [cited by examiner]
KR 1020220126063A · 2022 [cited by applicant]
KR 1020230044148A · 2023 [cited by applicant]
WO WO2022026692A1 · 2022 [cited by applicant]
Niemeyer et al. “RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs” [cited by applicant]
Chen et al. “Hallucinated Neural Radiance Fields in the Wild” [cited by applicant]
Lee et al. “ExtremeNeRF: Few-shot Neural Radiance Fields Under Unconstrained Illumination” arXiv preprint, arXiv:2303.11728v1 Mar. 21, 2023 (pp. 1-22). [cited by applicant]
Lee et al. “ExtremeNeRF: Few-shot Neural Radiance Fields Under Unconstrained Illumination” arXiv preprint, arXiv:2303.11728v2 Mar. 22, 2023 (pp. 1-22). [cited by applicant]
Jain, Ajay, et al., “Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis”, Proceedings of the IEEE/CVF International Conference on Computer Vision, arXiv:2104.00677v1, Apr. 1, 2021, (15 Pages in Engl… [cited by applicant]
Korean Office Action Issued on Jul. 21, 2025, in Counterpart Korean Patent Application No. 10-2023-0157935 (1 Page in English, 1 Page in Korean). [cited by applicant]