Published in IEEE Transactions on Geoscience and Remote Sensing, 2025
A reconstruction pipeline for forest structure that combines learned ranking with geometric and remote-sensing signals.
Recommended citation: Zhou, X., Li, B., Benes, B., Habib, A., Fei, S., Shao, J., & Pirk, S. (2025). "TreeStructor: Forest Reconstruction With Neural Ranking." IEEE Transactions on Geoscience and Remote Sensing, 63, 1-19.
Published in Plant Phenomics, 2025
A plant phenotyping study that uses 3D reconstruction to support quantitative analysis of phyllotaxy at scale.
Recommended citation: Davis, J. M., Gaillard, M., Tross, M. C., Shrestha, N., Ostermann, I., Grove, R. J., Li, B., Benes, B., & Schnable, J. C. (2025). "3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy." Plant Phenomics, 100023.
Published in ACM Transactions on Graphics, 2024
A tree modeling paper on volumetric structure, strands, and interactive control over rich arboreal detail.
Recommended citation: Li, B., Schwarz, N. A., Palubicki, W., Pirk, S., & Benes, B. (2024). "Interactive Invigoration: Volumetric Modeling of Trees with Strands." ACM Transactions on Graphics, 43(4).
Published in The Plant Phenome Journal, 2024
A phenotyping paper using synthetic training data to improve sorghum segmentation and leaf counting.
Recommended citation: Ostermann, I., Benes, B., Gaillard, M., Li, B., Davis, J., Grove, R., Shrestha, N., Tross, M. C., & Schnable, J. C. (2024). "Sorghum Segmentation and Leaf Counting Using In Silico Trained Deep Neural Model." The Plant Phenome Journal, 7(1), e70002.
Published in Computers and Electronics in Agriculture, 2024
A 3D point-cloud segmentation paper for separating nearby plant organs that share the same semantic class.
Recommended citation: Zarei, A., Li, B., Schnable, J. C., Lyons, E., Pauli, D., Barnard, K., & Benes, B. (2024). "PlantSegNet: 3D Point Cloud Instance Segmentation of Nearby Plant Organs with Identical Semantics." Computers and Electronics in Agriculture, 221, 108922.
Published in ACM Transactions on Graphics, 2023
A transformer-driven approach to tree generation that connects learned latent structure with L-system style procedural representations.
Recommended citation: Lee, J. J., Li, B., & Benes, B. (2023). "Latent L-Systems: Transformer-Based Tree Generator." ACM Transactions on Graphics, 43(1).
Published in ACM Transactions on Graphics, 2023
A model for coordinated tree growth above and below ground, linking canopy development with roots, soil, water, and nutrients.
Recommended citation: Li, B., Klein, J., Michels, D. L., Pirk, S., Benes, B., & Palubicki, W. (2023). "Rhizomorph: The Coordinated Function of Shoots and Roots." ACM Transactions on Graphics, 42(4).
Published in IEEE Transactions on Visualization and Computer Graphics, 2023
A learned representation for tree modeling that combines latent structure with context-aware generation.
Recommended citation: Zhou, X., Li, B., Benes, B., Fei, S., & Pirk, S. (2023). "DeepTree: Modeling Trees with Situated Latents." IEEE Transactions on Visualization and Computer Graphics, 1-14.
Published in ACM Transactions on Graphics, 2021
A method for reconstructing realistic botanical tree structure from a single image by combining learned priors and procedural growth.
Recommended citation: Li, B., Kaluzny, J., Klein, J., Michels, D. L., Palubicki, W., Benes, B., & Pirk, S. (2021). "Learning to Reconstruct Botanical Trees from Single Images." ACM Transactions on Graphics, 40(6).
Published in PeerJ, 2021
A 3D reconstruction and plant analysis paper linking leaf-angle measurements to genetic variation in sorghum.
Recommended citation: Tross, M. C., Gaillard, M., Zwiener, M., Miao, C., Grove, R. J., Li, B., Benes, B., & Schnable, J. C. (2021). "3D Reconstruction Identifies Loci Linked to Variation in Angle of Individual Sorghum Leaves." PeerJ, 9, e12628.
Published in SIGGRAPH Conference Papers, 2025
An interactive tree modeling paper focused on breakage behavior and strand-based structural control.
Recommended citation: Li, B., Schwarz, N., Palubicki, W., Pirk, S., Michels, D. L., & Benes, B. (2025). "Stressful Tree Modeling: Breaking Branches with Strands." SIGGRAPH Conference Papers.
Published in European Conference on Computer Vision, 2025
A dataset and reconstruction effort that turns single-image tree inputs into simulation-ready assets with diffusion-based priors.
Recommended citation: Lee, J. J., Li, B., Beery, S., Huang, J., Fei, S., Yeh, R. A., & Benes, B. (2025). "Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors." European Conference on Computer Vision, 439-460.