Neural Hierarchical Decomposition for Single Image Plant Modeling
Obtaining high-quality, practically usable 3D models of biological plants remains a significant challenge in computer vision and graphics. In this paper, we present a novel method for generating realistic 3D plant models from single-view photographs. Our approach employs a neural decomposition technique to learn a lightweight hierarchical box representation from the image, effectively capturing the structures and botanical features of plants. Then, this representation can be subsequently refined through a shape-guided parametric modeling module to produce complete 3D plant models. By combining hierarchical learning and parametric modeling, our method generates structured 3D plant assets with fine geometric details. Notably, through learning the decomposition in different levels of detail, our method can adapt to two distinct plant categories: outdoor trees and houseplants, each with unique appearance features. Within the scope of plant modeling, our method is the first comprehensive solution capable of reconstructing both plant categories from single-view images.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction
In the design of surgical guides for implant placement, determining the precise implant position is a critical step. However, the implant region itself is often characterized by a lack of distinctive texture in medical i…
RetinexFlow for CT metal artifact reduction
Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult. However, previous methods either require prior knowledge of the loc…
Computed Tomography (CT)Metal Artifact ReductionInDeed: Interpretable image deep decomposition with guaranteed generalizability
Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to the analysis. Deep learning can be powerfu…
Anomaly DetectionDenoisingImage DenoisingTest-time Adaptation+1Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis
There is a significant demand for topic modeling on large-scale data with complex multi-modal structure in applications such as multi-layer network analysis, temporal document classification, and video data analysis; fre…
Document ClassificationDetectability of hierarchical communities in networks
We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below w…