Athanasios Bacharis

Athanasios Bacharis, Konstantinos D. Polyzos, Henry J. Nelson et al.

3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. This task can be carried out via view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information, improving the resulting 3D reconstruction. Nonetheless, in most real-world settings, existing environmental noise can significantly affect the performance of 3D reconstruction. To that end, this work advocates a novel geometric-based reconstruction quality function for VP, that accounts for the existing noise of the environment, without requiring its closed-form expression. With no analytic expression of the objective function, this work puts forth an adaptive Bayesian optimization algorithm for accurate 3D reconstruction in the presence of noise. Numerical tests on noisy agricultural environments showcase the merits of the proposed approach for 3D reconstruction with even a small number of available cameras.

Chi-Shiang Gau, Konstantinos D. Polyzos, Athanasios Bacharis et al.

3D scene reconstruction and rendering are core tasks in computer vision, with applications spanning industrial monitoring, robotics, and autonomous driving. Recent advances in 3D Gaussian Splatting (GS) and its variants have achieved impressive rendering fidelity while maintaining high computational and memory efficiency. However, conventional vision-based GS pipelines typically rely on a sufficient number of camera views to initialize the Gaussian primitives and train their parameters, typically incurring additional processing cost during initialization while falling short in conditions where visual cues are unreliable, such as adverse weather, low illumination, or partial occlusions. To cope with these challenges, and motivated by the robustness of radio-frequency (RF) signals to weather, lighting, and occlusions, we introduce a multimodal framework that integrates RF sensing, such as automotive radar, with GS-based rendering as a more efficient and robust alternative to vision-only GS rendering. The proposed approach enables efficient depth prediction from only sparse RF-based depth measurements, yielding a high-quality 3D point cloud for initializing Gaussian functions across diverse GS architectures. Numerical tests demonstrate the merits of judiciously incorporating RF sensing into GS pipelines, achieving high-fidelity 3D scene rendering driven by RF-informed structural accuracy.

Konstantinos D. Polyzos, Athanasios Bacharis, Saketh Madhuvarasu et al.

Gaussian splatting (GS) along with its extensions and variants provides outstanding performance in real-time scene rendering while meeting reduced storage demands and computational efficiency. While the selection of 2D images capturing the scene of interest is crucial for the proper initialization and training of GS, hence markedly affecting the rendering performance, prior works rely on passively and typically densely selected 2D images. In contrast, this paper proposes `ActiveInitSplat', a novel framework for active selection of training images for proper initialization and training of GS. ActiveInitSplat relies on density and occupancy criteria of the resultant 3D scene representation from the selected 2D images, to ensure that the latter are captured from diverse viewpoints leading to better scene coverage and that the initialized Gaussian functions are well aligned with the actual 3D structure. Numerical tests on well-known simulated and real environments demonstrate the merits of ActiveInitSplat resulting in significant GS rendering performance improvement over passive GS baselines, in the widely adopted LPIPS, SSIM, and PSNR metrics.

Athanasios Bacharis, Konstantinos D. Polyzos, Georgios B. Giannakis et al.

Active vision (AV) has been in the spotlight of robotics research due to its emergence in numerous applications including agricultural tasks such as precision crop monitoring and autonomous harvesting to list a few. A major AV problem that gained popularity is the 3D reconstruction of targeted environments using 2D images from diverse viewpoints. While collecting and processing a large number of arbitrarily captured 2D images can be arduous in many practical scenarios, a more efficient solution involves optimizing the placement of available cameras in 3D space to capture fewer, yet more informative, images that provide sufficient visual information for effective reconstruction of the environment of interest. This process termed as view planning (VP), can be markedly challenged (i) by noise emerging in the location of the cameras and/or in the extracted images, and (ii) by the need to generalize well in other unknown similar agricultural environments without need for re-optimizing or re-training. To cope with these challenges, the present work presents a novel VP framework that considers a reconstruction quality-based optimization formulation that relies on the notion of `structure-from-motion' to reconstruct the 3D structure of the sought environment from the selected 2D images. With no analytic expression of the optimization function and with costly function evaluations, a Bayesian optimization approach is proposed to efficiently carry out the VP process using only a few function evaluations, while accounting for different noise cases. Numerical tests on both simulated and real agricultural settings signify the benefits of the advocated VP approach in efficiently estimating the optimal camera placement to accurately reconstruct 3D environments of interest, and generalize well on similar unknown environments.