In this paper, we introduce DecoRec, a novel system designed to elevate single-view 2D images to a decomposed 3D scene mesh. Current methods for single-view scene reconstruction typically rely on object retrieval or the regression of coarse 3D voxels or surfaces, leading to inaccuracies in capturing the appearance and geometry of the input image. The lack of high-quality large-scale scene-level datasets further complicates direct 3D scene generation from single-view images. To achieve high-quality 3D scene generation from a single-view image, DecoRec takes advantage of recent diffusion-based single-view object reconstruction methods to reconstruct individual objects separately. Subsequently, a refinement pipeline is proposed to effectively merge these reconstructed objects, enhancing appearance and geometry through a differentiable rendering technique and diffusion-guided refinement. Our results demonstrate that DecoRec facilitates high-quality single-view scene reconstruction in both geometry and novel synthesis, offering significant benefits for downstream applications like room interior design.

Neural implicit representations are increasingly used to depict three-dimensional (3D) shapes owing to their inherent smoothness and compactness, contrasting with traditional discrete representations. Yet, the multilayer perceptron–based neural representation, because of its smooth nature, rounds sharp corners or edges, rendering it unsuitable for representing objects with sharp features like computer-aided design (CAD) models. Moreover, neural implicit representations need long training times to fit 3D shapes. While previous works address these issues separately, we present a unified neural implicit representation called Patch-Grid , which efficiently fits complex shapes, preserves sharp features delineating different patches, and can also represent surfaces with open boundaries and thin geometric features. Patch-Grid learns a signed distance field (SDF) to approximate an encompassing surface patch of the shape with a learnable patch feature volume. To form sharp edges and corners in a CAD model, Patch-Grid merges the learned SDFs via the constructive solid geometry (CSG) approach. Core to the merging process is a novel merge grid design that organizes different patch feature volumes in a common octree structure. This design choice ensures robust merging of multiple learned SDFs by confining the CSG operations to localized regions. Additionally, it drastically reduces the complexity of the CSG operations in each merging cell, allowing the proposed method to be trained in seconds to fit a complex shape at high fidelity. Experimental results demonstrate that the proposed Patch-Grid representation is capable of accurately reconstructing shapes with complex sharp features, open boundaries, and thin geometric elements, achieving state-of-the-art reconstruction quality with high computational efficiency within seconds.

While tooth alignment is crucial for digital dentistry, especially in orthodontic treatment, existing computer-aided methods mainly focus on the 3D dental crown but overlook the entire teeth, which is essential for applications in orthodontics. Besides, clinical orthodontic rules are not fully considered in these methods, i.e., there should be no collisions and gaps between teeth, the upper jaw and lower jaw should have correct occlusion relationships, the teeth should comply with a reasonable dental arch curve, etc. To generate optimal tooth alignment results, we propose a rule-based optimization method for solving the tooth alignment problem that takes into consideration the clinical rules functionally and aesthetically. We optimize rule-driven objective functions by adjusting the 6-DoF transformations of each tooth. Besides, our optimization formulation supports customization for different clinical scenarios by specifying the various energy terms. Extensive experiments, ablation studies, and user studies have been conducted to validate the effectiveness of our method. Quantitative and qualitative comparisons demonstrate that our method generates better tooth alignments than previous methods.