Dense and Globally Consistent Multi-View Stereo

Multi-View Stereo (MVS) aims at reconstructing dense geometry of scenes from a set of overlapping images which are captured at different viewing angles. This thesis is devoted to addressing MVS problem by estimating depth maps, since 2D-space operations are trivially parallelizable in contrast to 3D volumetric techniques. Typical setup of depth-map-based MVS approaches consists of per-view calculation and multi-view merging. Most solutions primarily aim at the most precise and complete surfaces for individual views but relaxing the global geometry consistency. Therefore, the inconsistent estimates lead to heavy processing workload in the merging stage and diminish the final reconstruction. Another issue is the textureless areas where the photo-consistency constraint can not discriminate different depths. These matching ambiguities are normally handled by incorporating plane features or the smoothness assumption, that might produce segmentation effect or depends on accuracy and completeness of the calculated object edges. This thesis deals with two kinds of input data, photo collections and high-frame-rate videos, by developing distinct MVS algorithms based on their characteristics: For the sparsely sampled photos, we propose an advanced PatchMatch system that alternates between patch-based correlation maximization and pixel-based optimization of the cross-view consistency. Thereby we get a good trade-off between the photometric and geometric constraints. Moreover, our method achieves high efficiency by combining local pixel traversal and a hierarchical framework for fast depth propagation. For the densely sampled videos, we mainly focus on recovering the homogeneous surfaces, because the redundant scene information enables ray-level correlation which can generate shape depth discontinuities. Our approach infers smooth surfaces for the enclosed areas using perspective depth interpolation, and subsequently tackles the occlusion errors connecting the fore- and background edges. In addition, our edge depth estimation is more robust by accounting for unstructured camera trajectories. Exhaustively calculating depth maps is unfeasible when modeling large scenes from videos. This thesis further improves the reconstruction scalability using an incremental scheme via content-aware view selection and clustering. Our goal is to gradually eliminate the visibility conflicts and increase the surface coverage by processing a minimum subset of views. Constructing view clusters allows us to store merged and locally consistent points with the highest resolution, thus reducing the memory requirements. All approaches presented in the thesis do not rely on high-level techniques, so they can be easily parallelized. The evaluations on various datasets and the comparisons with existing algorithms demonstrate the superiority of our methods

Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video

In this tech report, we present the current state of our ongoing work on reconstructing Neural Radiance Fields (NERF) of general non-rigid scenes via ray bending. Non-rigid NeRF (NR-NeRF) takes RGB images of a deforming object (e.g., from a monocular video) as input and then learns a geometry and appearance representation that not only allows to reconstruct the input sequence but also to re-render any time step into novel camera views with high fidelity. In particular, we show that a consumer-grade camera is sufficient to synthesize convincing bullet-time videos of short and simple scenes. In addition, the resulting representation enables correspondence estimation across views and time, and provides rigidity scores for each point in the scene. We urge the reader to watch the supplemental videos for qualitative results. We will release our code

Fast and Accurate Depth Estimation from Sparse Light Fields

We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used as basic data units for depth estimation and refinement. Superpixel representation provides a desirable reduction in the computational cost while preserving the image geometry with respect to the object contours. Each superpixel is modeled as a plane in the image space, allowing depth values to vary smoothly within the superpixel area. Initial depth maps, which are obtained by plane sweeping, are iteratively refined by propagating good correspondences within an image. To ensure the fast convergence of the iterative optimization process, we employ a highly parallel propagation scheme that operates on all the superpixels of all the images at once, making full use of the parallel graphics hardware. A few optimization iterations of the energy function incorporating superpixel-wise smoothness and geometric consistency constraints allows to recover depth with high accuracy in textured and textureless regions as well as areas with occlusions, producing dense globally consistent depth maps. We demonstrate that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results.Comment: 15 pages, 15 figure

Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges

A One Stop 3D Target Reconstruction and multilevel Segmentation Method

3D object reconstruction and multilevel segmentation are fundamental to computer vision research. Existing algorithms usually perform 3D scene reconstruction and target objects segmentation independently, and the performance is not fully guaranteed due to the challenge of the 3D segmentation. Here we propose an open-source one stop 3D target reconstruction and multilevel segmentation framework (OSTRA), which performs segmentation on 2D images, tracks multiple instances with segmentation labels in the image sequence, and then reconstructs labelled 3D objects or multiple parts with Multi-View Stereo (MVS) or RGBD-based 3D reconstruction methods. We extend object tracking and 3D reconstruction algorithms to support continuous segmentation labels to leverage the advances in the 2D image segmentation, especially the Segment-Anything Model (SAM) which uses the pretrained neural network without additional training for new scenes, for 3D object segmentation. OSTRA supports most popular 3D object models including point cloud, mesh and voxel, and achieves high performance for semantic segmentation, instance segmentation and part segmentation on several 3D datasets. It even surpasses the manual segmentation in scenes with complex structures and occlusions. Our method opens up a new avenue for reconstructing 3D targets embedded with rich multi-scale segmentation information in complex scenes. OSTRA is available from https://github.com/ganlab/OSTRA