FruitNeRF: A Unified Neural Radiance Field based Fruit Counting
  Framework
We introduce FruitNeRF, a unified novel fruit counting framework that
leverages state-of-the-art view synthesis methods to count any fruit type
directly in 3D. Our framework takes an unordered set of posed images captured
by a monocular camera and segments fruit in each image. To make our system
independent of the fruit type, we employ a foundation model that generates
binary segmentation masks for any fruit. Utilizing both modalities, RGB and
semantic, we train a semantic neural radiance field. Through uniform volume
sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By
applying cascaded clustering on the extracted point cloud, our approach
achieves precise fruit count.The use of neural radiance fields provides
significant advantages over conventional methods such as object tracking or
optical flow, as the counting itself is lifted into 3D. Our method prevents
double counting fruit and avoids counting irrelevant fruit.We evaluate our
methodology using both real-world and synthetic datasets. The real-world
dataset consists of three apple trees with manually counted ground truths, a
benchmark apple dataset with one row and ground truth fruit location, while the
synthetic dataset comprises various fruit types including apple, plum, lemon,
pear, peach, and mango.Additionally, we assess the performance of fruit
counting using the foundation model compared to a U-Net.
66bad38a1caaa1d77c8a080e
Lukas Meyer
admin_assigned
2024-08-13T10:07:10.663Z
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Meyer
user
meyerls
66bad38a1caaa1d77c8a080f
Andreas Gilson
66bad38a1caaa1d77c8a0810
Ute Schmidt
66bad38a1caaa1d77c8a0811
Marc Stamminger
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting
  Framework
We introduce FruitNeRF, a unified novel fruit counting framework that
leverages state-of-the-art view synthesis methods to count any fruit type
directly in 3D. Our framework takes an unordered set of posed images captured
by a monocular camera and segments fruit in each image. To make our system
independent of the fruit type, we employ a foundation model that generates
binary segmentation masks for any fruit. Utilizing both modalities, RGB and
semantic, we train a semantic neural radiance field. Through uniform volume
sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By
applying cascaded clustering on the extracted point cloud, our approach
achieves precise fruit count.The use of neural radiance fields provides
significant advantages over conventional methods such as object tracking or
optical flow, as the counting itself is lifted into 3D. Our method prevents
double counting fruit and avoids counting irrelevant fruit.We evaluate our
methodology using both real-world and synthetic datasets. The real-world
dataset consists of three apple trees with manually counted ground truths, a
benchmark apple dataset with one row and ground truth fruit location, while the
synthetic dataset comprises various fruit types including apple, plum, lemon,
pear, peach, and mango.Additionally, we assess the performance of fruit
counting using the foundation model compared to a U-Net.
FruitNeRF uses semantic neural radiance fields to count fruits from unordered images, achieving precise 3D fruit counting without type dependency.
neural radiance fields semantic neural radiance fields uniform volume sampling cascaded clustering implicit Fruit Field