An official website of the United States government
We propose a boundary-aware multi-task deep-learning- based framework for fast 3D building modeling from a sin- gle overhead image. Unlike most existing techniques which rely on multiple images for 3D scene modeling, we seek to model the buildings in the scene from a single overhead im- age by jointly learning a modified signed distance function (SDF) from the building boundaries, a dense heightmap of the scene, and scene semantics. To jointly train for these tasks, we leverage pixel-wise semantic segmentation and normalized digital surface maps (nDSM) as supervision, in addition to labeled building outlines. At test time, buildings in the scene are automatically modeled in 3D using only an input overhead image. We demonstrate an increase in building modeling performance using a multi-feature net- work architecture that improves building outline detection by considering network features learned for the other jointly learned tasks. We also introduce a novel mechanism for ro- bustly refining instance-specific building outlines using the learned modified SDF. We verify the effectiveness of our method on multiple large-scale satellite and aerial imagery datasets, where we obtain state-of-the-art performance in the 3D building reconstruction task.
more »
« less
This content will become publicly available on March 17, 2026
TAVIC-DAS: Task and Channel-Aware Variable-Rate Image Compression for Distributed Autonomous System
In network-constrained environments, distributed multi-agent systems—such as UGVs and UAVs—must communicate effectively to support computationally demanding scene perception tasks like semantic and instance segmentation. These tasks are challenging because they require high accuracy even when using low-quality images, and the network limitations restrict the amount of data that can be transmitted between agents. To overcome the above challenges, we propose TAVIC-DAS to perform a task and channel-aware variable-rate image compression to enable distributed task execution and minimize communication latency by transmitting compressed images. TAVIC-DAS proposes a novel image compression and decompression framework (distributed across agents) that integrates channel parameters such as RSSI and data rate into a task-specific "semantic segmentation" DNN to generate masks representing the object of interest in the scene (ROI maps) by determining a high pixel density needed to represent objects of interest and low density to represents surrounding pixels within an image. Additionally, to accommodate agents with limited computational resources, TAVIC-DAS incorporates resource-aware model quantization. We evaluated TAVIC-DAS on platforms such as ROSMaster X3 and Jetson Xavier, which communicated using a low-frequency proprietary Doodle radio operating at 915 MHz. The experimental results show that TAVIC-DAS achieves approximately 7.62% higher PSNR and is about 6.39% more resource efficient compared to state-of-the-art techniques.
more »
« less
- PAR ID:
- 10612343
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-3553-7
- Page Range / eLocation ID:
- 255 to 260
- Format(s):
- Medium: X
- Location:
- Washington DC, DC, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at complex scene understanding lack representational power, efficiency, and the ability to create robust meta- knowledge about scenes. We introduce scenarios as a new way of representing scenes. The scenario is an interpretable, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects that is useful for a wide range of scene under- standing tasks. Scenarios are learned from data using a novel matrix factorization method which is integrated into a new neural network architecture, the Scenari-oNet. Using ScenarioNet, we can recover semantic in- formation about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classification, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. ScenarioNet is efficient because it requires significantly fewer parameters than other CNNs while achieving similar performance on benchmark tasks, and it is interpretable because it produces evidence in an understandable format for every decision it makes. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets.more » « less
-
Integrating multimodal data such as RGB and LiDAR from multiple views significantly increases computational and communication demands, which can be challenging for resource-constrained autonomous agents while meeting the time-critical deadlines required for various mission-critical applications. To address this challenge, we propose CoOpTex, a collaborative task execution framework designed for cooperative perception in distributed autonomous systems (DAS). CoOpTex contribution is twofold: (a) CoOpTex fuses multiview RGB images to create a panoramic camera view for 2D object detection and utilizes 360° LiDAR for 3D object detection, improving accuracy with a lightweight Graph Neural Network (GNN) that integrates object coordinates from both perspectives, (b) To optimize task execution and meet the deadline, CoOpTex dynamically offloads computationally intensive image stitching tasks to auxiliary devices when available and adjusts frame capture rates for RGB frames based on device mobility and processing capabilities. We implement CoOpTex in real-time on static and mobile heterogeneous autonomous agents, which helps to significantly reduce deadline violations by 100% while improving frame rates for 2D detection by 2.2 times in stationary and 2 times in mobile conditions, demonstrating its effectiveness in enabling real-time cooperative perception.more » « less
-
Abstract Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data that greatly reduces the utility of such 3D data, especially in crowded sample space with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the projection enhancement network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks.more » « less
-
Major semantic segmentation approaches are designed for RGB color images, which is interpolated from raw Bayer images. The use of RGB images on the one hand provides abundant scene color information. On the other hand, RGB images are easily observable for human users to understand the scene. The RGB color continuity also facilitates researchers to design segmentation algorithms, which becomes unnecessary in end-to-end learning. More importantly, the use of 3 channels adds extra storage and computation burden for neural networks. In contrast, the raw Bayer images can reserve the primitive color information in the largest extent with just a single channel. The compact design of Bayer pattern not only potentially increases a higher segmentation accuracy because of avoiding interpolation, but also significantly decreases the storage requirement and computation time in comparison with standard R, G, B images. In this paper, we propose BayerSeg-Net to segment single channel raw Bayer image directly. Different from RGB color images that already contain neighboring context information during ISP color interpolation, each pixel in raw Bayer images does not contain any context clues. Based on Bayer pattern properties, BayerSeg-Net assigns dynamic attention on Bayer images' spectral frequency and spatial locations to mitigate classification confusion, and proposes a re-sampling strategy to capture both global and local contextual information. We demonstrate the usability of raw Bayer images in segmentation tasks and the efficiency of BayerSeg-Net on multiple datasets.more » « less
