163 research outputs found
Binary Radiance Fields
In this paper, we propose binary radiance fields (BiRF), a storage-efficient
radiance field representation employing binary feature encoding that encodes
local features using binary encoding parameters in a format of either or
. This binarization strategy lets us represent the feature grid with highly
compact feature encoding and a dramatic reduction in storage size. Furthermore,
our 2D-3D hybrid feature grid design enhances the compactness of feature
encoding as the 3D grid includes main components while 2D grids capture
details. In our experiments, binary radiance field representation successfully
outperforms the reconstruction performance of state-of-the-art (SOTA) efficient
radiance field models with lower storage allocation. In particular, our model
achieves impressive results in static scene reconstruction, with a PSNR of
31.53 dB for Synthetic-NeRF scenes, 34.26 dB for Synthetic-NSVF scenes, 28.02
dB for Tanks and Temples scenes while only utilizing 0.7 MB, 0.8 MB, and 0.8 MB
of storage space, respectively. We hope the proposed binary radiance field
representation will make radiance fields more accessible without a storage
bottleneck.Comment: 21 pages, 12 Figures, and 11 Table
Style-transfer GANs for bridging the domain gap in synthetic pose estimator training
Given the dependency of current CNN architectures on a large training set,
the possibility of using synthetic data is alluring as it allows generating a
virtually infinite amount of labeled training data. However, producing such
data is a non-trivial task as current CNN architectures are sensitive to the
domain gap between real and synthetic data. We propose to adopt general-purpose
GAN models for pixel-level image translation, allowing to formulate the domain
gap itself as a learning problem. The obtained models are then used either
during training or inference to bridge the domain gap. Here, we focus on
training the single-stage YOLO6D object pose estimator on synthetic CAD
geometry only, where not even approximate surface information is available.
When employing paired GAN models, we use an edge-based intermediate domain and
introduce different mappings to represent the unknown surface properties. Our
evaluation shows a considerable improvement in model performance when compared
to a model trained with the same degree of domain randomization, while
requiring only very little additional effort
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