We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric constraints (eg. epipolar geometry) as well as adaptive photometric costs that support multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated video, and 3) we propose several online finetuning strategies that rely on the symmetry of our self-supervised loss in both training and testing, in particular optimizing model parameters and or the output of different tasks, leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches on multiple tasks. We also show good generalization for transfer learning.
Understanding the geometry and dynamics of 3D scenes usually involves estimating multiple properties including depth, camera motion or optical flow. (SfM) @cite_34 @cite_33 @cite_14 @cite_21 @cite_40 @cite_19 or scene flow estimation @cite_25 @cite_42 are well-established methodologies with a solid record of fundamental and practical progress. Classical approaches are based on low-level feature matching followed by geometric verification using algebraic relations, the epipolar constraint, in conjunction with RANSAC @cite_39 , and bundle adjustment @cite_16 . Models for structure and motion estimation based on such pipelines have produced impressive results @cite_54 , but their reconstructions are often sparse and prone to error in textureless or occluded areas.
@cite_14 @cite_33 @cite_54 @cite_21 @cite_42 @cite_16 @cite_39 @cite_19 @cite_40 @cite_34 @cite_25
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3D scene flow estimation aims to jointly recover dense geometry and 3D motion from stereoscopic image sequences, thus generalizes classical disparity and 2D optical flow estimation. To realize its conceptual benefits and overcome limitations of many existing methods, we propose to represent the dynamic scene as a collection of rigidly moving planes, into which the input images are segmented. Geometry and 3D motion are then jointly recovered alongside an over-segmentation of the scene. This piecewise rigid scene model is significantly more parsimonious than conventional pixel-based representations, yet retains the ability to represent real-world scenes with independent object motion. It, furthermore, enables us to define suitable scene priors, perform occlusion reasoning, and leverage discrete optimization schemes toward stable and accurate results. Assuming the rigid motion to persist approximately over time additionally enables us to incorporate multiple frames into the inference. To that end, each view holds its own representation, which is encouraged to be consistent across all other viewpoints and frames in a temporal window. We show that such a view-consistent multi-frame scheme significantly improves accuracy, especially in the presence of occlusions, and increases robustness against adverse imaging conditions. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo. We present an approach to jointly estimating camera motion and dense structure of a static scene in terms of depth maps from monocular image sequences in driver-assistance scenarios. At each instant of time, only two consecutive frames are processed as input data of a joint estimator that fully exploits second-order information of the corresponding optimization problem and effectively copes with the non-convexity due to both the imaging geometry and the manifold of motion parameters. Additionally, carefully designed Gaussian approximations enable probabilistic inference based on locally varying confidence and globally varying sensitivity due to the epipolar geometry, with respect to the high-dimensional depth map estimation. Embedding the resulting joint estimator in an online recursive framework achieves a pronounced spatio-temporal filtering effect and robustness. We evaluate hundreds of images taken from a car moving at speed up to 100 km h and being part of a publicly available benchmark data set. The results compare favorably with two alternative settings: stereo based scene reconstruction and camera motion estimation in batch mode using multiple frames. They, however, require a calibrated camera pair or storage for more than two frames, which is less attractive from a technical viewpoint than the proposed monocular and recursive approach. In addition to real data, a synthetic sequence is considered which provides reliable ground truth. Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this paper, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in the scene (Fig. 1). These properties allow us to recover not only the structure and motion but also the 3D locations, poses, and categories of objects in the scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-point-based SFM algorithms, iii) improve object detections given multiple un-calibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets–LiDAR cars, street-view pedestrians, and Kinect office desktop–verify our theoretical claims. We present a new structure from motion (Sfm) technique based on point and vanishing point (VP) matches in images. First, all global camera rotations are computed from VP matches as well as relative rotation estimates obtained from pairwise image matches. A new multi-staged linear technique is then used to estimate all camera translations and 3D points simultaneously. The proposed method involves first performing pairwise reconstructions, then robustly aligning these in pairs, and finally aligning all of them globally by simultaneously estimating their unknown relative scales and translations. In doing so, measurements inconsistent in three views are efficiently removed. Unlike sequential Sfm, the proposed method treats all images equally, is easy to parallelize and does not require intermediate bundle adjustments. There is also a reduction of drift and significant speedups up to two order of magnitude over sequential Sfm. We compare our method with a standard Sfm pipeline [1] and demonstrate that our linear estimates are accurate on a variety of datasets, and can serve as good initializations for final bundle adjustment. Because we exploit VPs when available, our approach is particularly well-suited to the reconstruction of man-made scenes.