Modern incremental learning for semantic segmentation methods usually learn new categories based on dense annotations. Although achieve promising results, pixel-by-pixel labeling is costly and time-consuming. Weakly incremental learning for semantic segmentation (WILSS) is a novel and attractive task, which aims at learning to segment new classes from cheap and widely available image-level labels. Despite the comparable results, the image-level labels can not provide details to locate each segment, which limits the performance of WILSS. This inspires us to think how to improve and effectively utilize the supervision of new classes given image-level labels while avoiding forgetting old ones. In this work, we propose a novel and data-efficient frame-work for WILSS, named FMWISS. Specifically, we propose pre-training based co-segmentation to distill the knowledge of complementary foundation models for generating dense pseudo labels. We further optimize the noisy pseudo masks with a teacher-student architecture, where a plug-in teacher is optimized with a proposed dense contrastive loss. Moreover, we introduce memory-based copy-paste augmentation to improve the catastrophic forgetting problem of old classes. Extensive experiments on Pascal VOC and COCO datasets demonstrate the superior performance of our framework, e.g., FMWISS achieves 70.7% and 73.3% in the 15–5 VOC setting, outperforming the state-of-the-art method by 3.4% and 6.1%, respectively.

Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise the generation of 3D models, e.g., NeRF. However, score distillation is prone to suffer the view inconsistency problem, and implicit NeRF modeling can also lead to an arbitrary shape, thus leading to less realistic and uncontrollable 3D generation. In this work, we propose a flexible framework of Points-to-3D to bridge the gap between sparse yet freely available 3D points and realistic shape-controllable 3D generation by distilling the knowledge from both 2D and 3D diffusion models. The core idea of Points-to-3D is to introduce controllable sparse 3D points to guide the text-to-3D generation. Specifically, we use the sparse point cloud generated from the 3D diffusion model, Point-E, as the geometric prior, conditioned on a single reference image. To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points. In addition to controlling the geometry, we propose to optimize the NeRF for a more view-consistent appearance. To be specific, we perform score distillation to the publicly available 2D image diffusion model ControlNet, conditioned on text as well as depth map of the learned compact geometry. Qualitative and quantitative comparisons demonstrate that Points-to-3D improves view consistency and achieves good shape controllability for text-to-3D generation. Points-to-3D provides users with a new way to improve and control text-to-3D generation.

Multi-view Stereo (MVS) aims to reconstruct a 3D point cloud model from multiple views. In recent years, learning-based MVS methods have received a lot of attention and achieved excellent performance compared with traditional methods. However, these methods still have apparent shortcomings, such as the accumulative error in the coarse-to-fine strategy and the inaccurate depth hypotheses based on the uniform sampling strategy. In this paper, we propose the NR-MVSNet, a coarse-to-fine structure with the depth hypotheses based on the normal consistency (DHNC) module, and the depth refinement with reliable attention (DRRA) module. Specifically, we design the DHNC module to generate more effective depth hypotheses, which collects the depth hypotheses from neighboring pixels with the same normals. As a result, the predicted depth can be smoother and more accurate, especially in texture-less and repetitive-texture regions. On the other hand, we update the initial depth map in the coarse stage by the DRRA module, which can combine attentional reference features and cost volume features to improve the depth estimation accuracy in the coarse stage and address the accumulative error problem. Finally, we conduct a series of experiments on the DTU, BlendedMVS, Tanks & Temples, and ETH3D datasets. The experimental results demonstrate the efficiency and robustness of our NR-MVSNet compared with the state-of-the-art methods. Our implementation is available at https://github.com/wdkyh/NR-MVSNet.

In recent years, supervised or unsupervised learning-based MVS methods achieved excellent performance compared with traditional methods. However, these methods only use the probability volume computed by cost volume regularization to predict reference depths and this manner cannot mine enough information from the probability volume. Furthermore, the unsupervised methods usually try to use two-step or additional inputs for training which make the procedure more complicated. In this paper, we propose the DS-MVSNet, an end-to-end unsupervised MVS structure with the source depths synthesis. To mine the information in probability volume, we creatively synthesize the source depths by splattering the probability volume and depth hypotheses to source views. Meanwhile, we propose the adaptive Gaussian sampling and improved adaptive bins sampling approach that improve the depths hypotheses accuracy. On the other hand, we utilize the source depths to render the reference images and propose depth consistency loss and depth smoothness loss. These can provide additional guidance according to photometric and geometric consistency in different views without additional inputs. Finally, we conduct a series of experiments on the DTU dataset and Tanks $&$ Temples dataset that demonstrate the efficiency and robustness of our DS-MVSNet compared with the state-of-the-art methods.

Object detection algorithms, from high-resolution optical remote sensing images, have been booming from the last few years. However, object tracking for high-resolution optical remote sensing video is a challenging task due to the large number and small size of objects. In this paper, we propose an object detection and tracking method based on deep convolutional neural networks for wide swath high-resolution optical remote sensing videos. The proposed method firstly segments each frame of a video into sub-samples using a sliding window of fixed size. In order to detect the objects appearing at the edge of the sliding window efficiently, we use an overlapping sliding window sampling method. Further, we design a network fusing region of interests (RoIs) of the previous and current frames to track the objects occurred in the previous frames of the video. RoIs of previous frame are applied directly to the feature layer of the current frame. Finally, for each frame, we merge the detection and tracking results of sub-samples by non-maximum suppression (NMS) method. The experimental results on our dataset demonstrate the validity and generality of the proposed detection algorithm.

We present balancing control analysis of a stationary riderless motorcycle. We first present the motorcycle dynamics with an accurate steering mechanism model with consideration of lateral movement of the tire/ground contact point. A nonlinear balance controller is then designed. We estimate the domain of attraction (DOA) of motorcycle dynamics under which the stationary motorcycle can be stabilized by steering. For a typical motorcycle/bicycle configuration, we find that the DOA is relatively small and thus balancing control by only steering at stationary is challenging. The balance control and DOA estimation schemes are validated by experiments conducted on the Rutgers autonomous motorcycle. The attitudes of the motorcycle platform are obtained by a novel estimation scheme that fuses measurements from global positioning systems (GPS) and inertial measurement units (IMU). We also present the experiments of the GPS/IMU-based attitude estimation scheme in the paper.