Amit Regmi

Aditya Joglekar, Amit Regmi, Kenji Shimada et al.

Engineering design intent is often communicated through rasterized orthographic drawings. However, downstream workflows inherently require editable and parametrically defined 3D computer-aided design (CAD) models. To bridge this gap, we introduce Ortho2CAD, a vision-language model (VLM) specifically designed to translate rasterized orthographic drawings directly into editable CadQuery code, which can then be seamlessly converted into 3D CAD models. To train the model effectively, we utilize supervised fine-tuning (SFT) for instances where explicit CadQuery code labels already exist, and we apply geometry-grounded reinforcement learning (RL) to optimize the model in scenarios where ground-truth labels are absent. To enable learning at scale, we create a pythonOCC-based drawing generator that renders first-angle orthographic projections from STEP models, complete with dashed hidden lines and key dimensions. On existing datasets encompassing settings both with and without CadQuery supervision, we generate orthographic drawings and show that our model produces 100% syntactically valid code. Moreover, it achieves a 3D CAD intersection-over-union (IoU) accuracy that surpasses all baselines, with an average relative improvement of over 7% compared directly against the next best performing model. We show that leveraging VLMs with SFT and RL techniques can effectively pave the way forward for orthographic drawing to 3D CAD reconstruction. Our implementation is available at https://github.com/AdityaJoglekar/Ortho2CAD.

Rabin Kunwar, Dikshya Parajuli, Rujal Acharya et al.

Single-unit crown restoration is among the most common procedures in clinical dentistry, with CAD/CAM workflows now designing crowns directly from intraoral scans. Partial scans are often preferred over full-arch scans for single-unit cases due to fewer stitching errors, yet most segmentation networks trained on full arches fail on partial scans, while end-to-end generative crown methods often produce over-smoothed surfaces that lose occlusal detail. We propose an end-to-end pipeline that takes a raw intraoral scan and target FDI tooth number as input and outputs an initial, patient-specific crown proposal for clinician refinement. The pipeline has three phases: (I) data preparation and pose standardization; (II) segmentation routed by scan type; and (III) crown proposal generation via context-aware retrieval and Blender-based fitting. We address partial-scan segmentation through a classify-then-align strategy: a DGCNN classifier categorizes the scan into one of five anatomical types, then coarse-to-fine RANSAC+ICP registration standardizes the jaw coordinate frame, followed by graph-cut optimization to refine tooth-gingival boundaries. Trained on 1,958 partial scans, the pipeline achieves macro-average DSC 0.9249, Recall 0.8919, and Precision 0.9615 across 17 semantic classes; a fine-tuned full-arch model reaches DSC 0.9347. The prepared tooth and its mesial and distal neighbors achieve DSC 0.9468-0.9569 with sub-millimeter Centroid Errors (0.2666-0.2774 mm). These centroids anchor a retrieval module using DGCNN embeddings and cosine similarity over neighboring and opposing teeth, followed by spline-guided alignment and Blender Python API refinement. The pipeline produces a preliminary crown shell in 2.5-3.5 minutes, offering a practical alternative to end-to-end generative approaches.