While there has been significant progress to use simulated data to learn robotic manipulation of rigid objects, applying its success to deformable objects has been hindered by the lack of both deformable object models and realistic non-rigid body simulators. In this paper, we present Real Garment Benchmark (RGBench), a comprehensive benchmark for robotic manipulation of garments. It features a diverse set of over 6000 garment mesh models, a new high-performance simulator, and a comprehensive protocol to evaluate garment simulation quality with carefully measured real garment dynamics. Our experiments demonstrate that our simulator outperforms currently available cloth simulators by a large margin, reducing simulation error by 20% while maintaining a speed of 3 times faster. We will publicly release RGBench to accelerate future research in robotic garment manipulation.

Learning-based multiple view stereo has gained significant attention recently. However, most methods rely on direct network supervision using provided ground-truth depth, which poses three inherent problems: resolution-dependent ground-truth artifacts, excessively challenging training examples (with relatively featureless textures), and use of less-viewed reference pixels for supervision, all of which hinder network optimization. To alleviate these problems, we propose an accurate network supervision paradigm that includes a ground-truth mask, an entropy mask, and a consistency mask, which provide more accurate supervision signals to aid network optimization. Furthermore, we introduce UANet, an uncertainty aware multi-view stereo network, which adaptively determines a pixel-wise search range using a dynamic range sampler (DRS) built upon estimation confidence and learned uncertainty. Experimental results on recent MVS datasets demonstrate the effectiveness of our method.

TLS 1.3 is widely used to realize secure communication over the Internet. Existing security analyses of TLS 1.3 primarily focus on its handshake protocol which is indeed an authenticated key exchange (AKE) protocol, and implicitly neglect the so-called subversion attacks (e.g., breaking TLS via Dual EC) in the real world. Reverse firewall (RF) is a prevalent approach to defend against subversion attack. To the best of our knowledge, the only two subversion-resilient AKE protocols with RFs are proposed by Dodis et al. (CRYPTO'16) and Bossuat et al. (ESORICS'20). The security of both protocols is proved under game-based model which is insufficient for the concurrent execution of multiple TLS instances in practice. In this paper, we propose$\mathsf {srTLS}$, a variant of the TLS 1.3 full one round-trip time (1-RTT) handshake protocol with RFs under the universally composable (UC) model. In particular, we first present the ideal functionality of unilateral AKE$\mathcal {F}_{\mathsf {uaKE}}$. Then, we use RFs with outer transparency to circumvent the difficulty in sanitizing the messages of handshake protocol, and prove that$\mathsf {srTLS}$UC-realizes$\mathcal {F}_{\mathsf {uaKE}}$in the presence of subversion attacks. Finally, we integrate$\mathsf {srTLS}$and existing subversion-resilient AKE protocols into TLS 1.3. The evaluation result demonstrates that$\mathsf {srTLS}$achieves at least a 44.86% efficiency improvement over other subversion-resilient AKE protocols.

Compared to widely used LiDAR-based mapping in autonomous driving field, image-based mapping method has the advantages of low cost, high resolution, and no need for complex calibration. However, the image-based 3D mapping depends heavily on the texture richness and always leaves holes and outliers in low-textured areas, such as the road surface. To this end, this paper proposed a novel semantically guided Multi-View Stereo method for dense 3D road mapping, which integrates semantic information into PatchMatch-based MVS pipeline and uses image semantic segmentation as soft constraints in neighbor views selection, depth-map initialization, depth propagation, and depth-map completion. Experimental results on public and our own datasets show that, with the help of semantics, the proposed method achieves superior completeness with comparable accuracy for 3D road mapping compared to state-of-the-art MVS methods.