For autonomous vehicles, driving safely is highly dependent on the capability to correctly perceive the environment in the 3D space, hence the task of 3D object detection represents a fundamental aspect of perception. While 3D sensors deliver accurate metric perception, monocular approaches enjoy cost and availability advantages that are valuable in a wide range of applications. Unfortunately, training monocular methods requires a vast amount of annotated data. To compensate for this need, we propose a novel approach to self-supervise 3D object detection purely from RGB video sequences, leveraging geometric constraints and weak labels. Unlike other approaches that exploit additional sensors during training, our method relies on the temporal continuity of video sequences. A supervised pre-training on synthetic data produces initial plausible 3D boxes, then our geometric and photometrically grounded losses provide a strong self-supervision signal that allows the model to be fine-tuned on real data without labels. Our experiments on Autonomous Driving benchmark datasets showcase the effectiveness and generality of our approach and the competitive performance compared to other self-supervised approaches. • Self-supervised 3D object detection from RGB videos using geometric constraints. • Temporal continuity repaces additional sensors in training. • Self-supervision enables fine-tuning on real data without labels.

In mixed reality (MR) telepresence applications, the differences between participants' physical environments can interfere with effective collaboration. For asymmetric tasks, users might need to access different resources (information, objects, tools) distributed throughout their room. Existing intersection methods do not support such interactions, because a large portion of the telepresence participants' rooms become inaccessible, along with the relevant task resources. We propose MRUnion, a Mixed Reality Telepresence pipeline for asymmetric task-aware 3D mutual scene generation. The key concept of our approach is to enable a user in an asymmetric telecollaboration scenario to access the entire room, while still being able to communicate with remote users in a shared space. For this purpose, we introduce a novel mutual room layout called Union. We evaluated 882 space combinations quantitatively involving two, three, and four combined remote spaces and compared it to a conventional Intersect room layout. The results show that our method outperforms existing intersection methods and enables a significant increase in space and accessibility to resources within the shared space. In an exploratory user study (N=24), we investigated the applicability of the synthetic mutual scene in both MR and VR setups, where users collaborated on an asymmetric remote assembly task. The study results showed that our method achieved comparable results to the intersect method but requires further investigation in terms of social presence, safety and support of collaboration. From this study, we derived design implications for synthetic mutual spaces.

Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details. In this paper, we propose SCRREAM, a dataset annotation framework that allows annotation of fully dense meshes of objects in the scene and registers camera poses on the real image sequence, which can produce accurate ground truth for both sparse 3D as well as dense 3D tasks. We show the details of the dataset annotation pipeline and showcase four possible variants of datasets that can be obtained from our framework with example scenes, such as indoor reconstruction and SLAM, scene editing & object removal, human reconstruction and 6d pose estimation. Recent pipelines for indoor reconstruction and SLAM serve as new benchmarks. In contrast to previous indoor dataset, our design allows to evaluate dense geometry tasks on eleven sample scenes against accurately rendered ground truth depth maps.

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset11dataset available at https://github.com/Junggy/HAMMER-dataset comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion.

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price. Previous works have mainly focused on quality improvement for I-ToF imaging especially curing the effect of Multi Path Interference (MPI). These investigations are typically done in specifically constrained scenarios at close distance, indoors and under little ambient light. Surprisingly little work has investigated I-ToF quality improvement in real-life scenarios where strong ambient light and far distances pose difficulties due to an extreme amount of induced shot noise and signal sparsity, caused by the attenuation with limited sensor power and light scattering. In this work, we propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image and fuses their latent representation based on a multi step approach involving both implicit and explicit alignment to predict a high quality long range depth map aligned to the RGB viewpoint. We test our approach on challenging real-world scenes and show more than 40% RMSE improvement on the final depth map compared to the baseline approach [33].

This work proposes a RGB-D SLAM system specifically designed for structured environments and aimed at improved tracking and mapping accuracy by relying on geometric features that are extracted from the surrounding. Structured environments offer, in addition to points, also an abundance of geometrical features such as lines and planes, which we exploit to design both the tracking and mapping components of our SLAM system. For the tracking part, we explore geometric relationships between these features based on the assumption of a Manhattan World (MW). We propose a decoupling-refinement method based on points, lines, and planes, as well as the use of Manhattan relationships in an additional pose refinement module. For the mapping part, different levels of maps from sparse to dense are reconstructed at a low computational cost. We propose an instance-wise meshing strategy to build a dense map by meshing plane instances independently. The overall performance in terms of pose estimation and reconstruction is evaluated on public benchmarks and shows improved performance compared to state-of-the-art methods. The code is released at https://github.com/yanyan-li/PlanarSLAM.

Recent advances in monocular SLAM have enabled real-time capable systems which run robustly under the assumption of a static environment, but fail in presence of dynamic scene changes and motion, since they lack an explicit dynamic outlier handling. We propose a semantic monocular SLAM framework designed to deal with highly dynamic environments, combining feature-based and direct approaches to achieve robustness under challenging conditions. The proposed approach exploits semantic information extracted from the scene within an explicit probabilistic model, which maximizes the probability for both tracking and mapping to rely on those scene parts that do not present a relative motion with respect to the camera. We show more stable pose estimation in dynamic environments and comparable performance to the state of the art on static sequences on the Virtual KITTI and Synthia datasets.