In computer vision, the aim is to model and extract high-level information from visual sensor measurements such as images, videos and 3D points. Since visual data is often high-dimensional, noisy and irregular, achieving robust data modeling is challenging. This thesis presents works that address challenges within a number of different computer vision problems. 

First, the thesis addresses the problem of phase unwrapping for multi-frequency amplitude modulated time-of-flight (ToF) ranging. ToF is used in depth cameras, which have many applications in 3D reconstruction and gesture recognition. While amplitude modulation in time-of-flight ranging can provide accurate measurements for the depth, it also causes depth ambiguities. This thesis presents a method to resolve the ambiguities by estimating the likelihoods of different hypotheses for the depth values. This is achieved by performing kernel density estimation over the hypotheses in a spatial neighborhood of each pixel in the depth image. The depth hypothesis with the highest estimated likelihood can then be selected as the output depth. This approach yields improvements in the quality of the depth images and extends the effective range in both indoor and outdoor environments. 

Next, point set registration is investigated, which is the problem of aligning point sets from overlapping depth images or 3D models. Robust registration is fundamental to many vision tasks, such as multi-view 3D reconstruction and object pose estimation for robotics. The thesis presents a method for handling density variations in the measured point sets. This is achieved by modeling a latent distribution representing the underlying structure of the scene. Both the model of the scene and the registration parameters are inferred in an Expectation-Maximization based framework. Secondly, the thesis introduces a method for integrating features from deep neural networks into the registration model. It is shown that the deep features improve registration performance in terms of accuracy and robustness. Additionally, improved feature representations are generated by training the deep neural network end-to-end by minimizing registration errors produced by our registration model. 

Further, an approach for 3D point set segmentation is presented. As scene models are often represented using 3D point measurements, segmentation of these is important for general scene understanding. Learning models for segmentation requires a significant amount of annotated data, which is expensive and time-consuming to acquire. The approach presented in the thesis circumvents this by projecting the points into virtual camera views and render 2D images. The method can then exploit accurate convolutional neural networks for image segmentation and map the segmentation predictions back to the 3D points. This also allows for transferring learning using available annotated image data, thereby reducing the need for 3D annotations. 

Finally, the thesis explores the problem of video object segmentation (VOS), where the task is to track and segment target objects in each frame of a video sequence. Accurate VOS requires a robust model of the target that can adapt to different scenarios and objects. This needs to be achieved using only a single labeled reference frame as training data for each video sequence. To address the challenges in VOS, the thesis introduces a parametric target model, optimized to predict a target label derived from the mask annotation. The target model is integrated into a deep neural network, where its predictions guide a decoder module to produce target segmentation masks. The deep network is trained on labeled video data to output accurate segmentation masks for each frame. Further, it is shown that by training the entire network model in an end-to-end manner, it can learn a representation of the target that provides increased segmentation accuracy.