CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive
  Neural Rendering
This paper proposes a neural rendering approach that represents a scene as
"compressed light-field tokens (CLiFTs)", retaining rich appearance and
geometric information of a scene. CLiFT enables compute-efficient rendering by
compressed tokens, while being capable of changing the number of tokens to
represent a scene or render a novel view with one trained network. Concretely,
given a set of images, multi-view encoder tokenizes the images with the camera
poses. Latent-space K-means selects a reduced set of rays as cluster centroids
using the tokens. The multi-view ``condenser'' compresses the information of
all the tokens into the centroid tokens to construct CLiFTs. At test time,
given a target view and a compute budget (i.e., the number of CLiFTs), the
system collects the specified number of nearby tokens and synthesizes a novel
view using a compute-adaptive renderer. Extensive experiments on RealEstate10K
and DL3DV datasets quantitatively and qualitatively validate our approach,
achieving significant data reduction with comparable rendering quality and the
highest overall rendering score, while providing trade-offs of data size,
rendering quality, and rendering speed.
68745e0f257d4f0435370288
Zhengqing Wang
68745e0f257d4f0435370289
Yuefan Wu
68745e0f257d4f043537028a
Jiacheng Chen
claimed_verified
2025-07-15T19:10:47.200Z
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/avatars/1a187beb91a5e2fdc2303620b742aab1.svg
Jiacheng Chen
user
cccjc
68745e0f257d4f043537028b
Fuyang Zhang
68745e0f257d4f043537028c
Yasutaka Furukawa
CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive
  Neural Rendering
This paper proposes a neural rendering approach that represents a scene as
"compressed light-field tokens (CLiFTs)", retaining rich appearance and
geometric information of a scene. CLiFT enables compute-efficient rendering by
compressed tokens, while being capable of changing the number of tokens to
represent a scene or render a novel view with one trained network. Concretely,
given a set of images, multi-view encoder tokenizes the images with the camera
poses. Latent-space K-means selects a reduced set of rays as cluster centroids
using the tokens. The multi-view ``condenser'' compresses the information of
all the tokens into the centroid tokens to construct CLiFTs. At test time,
given a target view and a compute budget (i.e., the number of CLiFTs), the
system collects the specified number of nearby tokens and synthesizes a novel
view using a compute-adaptive renderer. Extensive experiments on RealEstate10K
and DL3DV datasets quantitatively and qualitatively validate our approach,
achieving significant data reduction with comparable rendering quality and the
highest overall rendering score, while providing trade-offs of data size,
rendering quality, and rendering speed.
A neural rendering approach using compressed light-field tokens (CLiFTs) achieves efficient rendering with adjustable token numbers, maintaining high quality and speed.
neural rendering compressed light-field tokens CLiFTs multi-view encoder latent-space K-means condenser compute-adaptive renderer RealEstate10K DL3DV datasets