Sketch-based modeling studies reconstructing models from sketches automatically, allowing users visualize design concepts rapidly. Generating CAD models based on user sketches helps reduce the learning curve for novice users, which promotes the everyday use of CAD software, and expands its reach to non-professional groups. While various algorithms study automatically generating models from single sketch or line drawing, they often produce non-editable models or editable models limited to simple extrusion operations. To improve this issue, we propose a novel sketch-based modeling system, Sketch2Seq, which generates complex, semantic, and editable CAD models. Our system eliminates the need for additional annotations from users and produces models that support subsequent application in commercial software. The core of our method lies in understanding users' design intent from CAD sketches. We design a novel sketch segmentation network for identifying diverse operation features in CAD sketches, which utilizes geometric features of strokes and different levels of topological connections. Additionally, to tackle the segmentation task, a dataset for CAD sketch segmentation is introduced. Comparative experiments and ablation evaluations prove the effectiveness of the proposed method. Based on segmentation result, coarse CAD sequences are generated and progressively executed. Meanwhile, the orders and parameters of the CAD sequences are optimized with context models and input sketches. All algorithms are integrated into a user interface. Experiments and evaluations validate the feasibility and superiority of our entire system which is able to reconstruct more complex features and achieve better results for longer sequence.

We propose a pixel-accurate failure prediction approach for semantic video segmentation. The proposed scheme improves previously proposed failure prediction methods which so far disregarded the temporal information in videos. Our approach consists of two main steps: First, we train an LSTM-based model to detect spatio-temporal patterns that indicate pixel-wise misclassifications in the current video frame. Second, we use sequences of failure predictions to train a denoising autoencoder that both refines the current failure prediction and predicts future misclassifications. Since public data sets for this scenario are limited, we introduce the large-scale densely annotated video driving (DAVID) data set generated using the CARLA simulator. We evaluate our approach on the real-world Cityscapes data set and the simulator-based DAVID data set. Our experimental results show that spatiotemporal failure prediction outperforms single-image failure prediction by up to 8.8%. Refining the prediction using a sequence of previous failure predictions further improves the performance by a significant 15.2% and allows to accurately predict misclassifications for future frames. While we focus our study on driving videos, the proposed approach is general and can be easily used in other scenarios as well.