Real-Time Semantic Stereo Matching (1910.00541v2)

Abstract: Scene understanding is paramount in robotics, self-navigation, augmented reality, and many other fields. To fully accomplish this task, an autonomous agent has to infer the 3D structure of the sensed scene (to know where it looks at) and its content (to know what it sees). To tackle the two tasks, deep neural networks trained to infer semantic segmentation and depth from stereo images are often the preferred choices. Specifically, Semantic Stereo Matching can be tackled by either standalone models trained for the two tasks independently or joint end-to-end architectures. Nonetheless, as proposed so far, both solutions are inefficient because requiring two forward passes in the former case or due to the complexity of a single network in the latter, although jointly tackling both tasks is usually beneficial in terms of accuracy. In this paper, we propose a single compact and lightweight architecture for real-time semantic stereo matching. Our framework relies on coarse-to-fine estimations in a multi-stage fashion, allowing: i) very fast inference even on embedded devices, with marginal drops in accuracy, compared to state-of-the-art networks, ii) trade accuracy for speed, according to the specific application requirements. Experimental results on high-end GPUs as well as on an embedded Jetson TX2 confirm the superiority of semantic stereo matching compared to standalone tasks and highlight the versatility of our framework on any hardware and for any application.

• The paper presents RTS²Net, a unified framework that fuses depth estimation with semantic segmentation for enhanced real-time scene understanding.
• It employs a coarse-to-fine multi-stage architecture, maintaining high accuracy while operating efficiently on both high-end GPUs and embedded systems.
• Experimental results on the KITTI 2015 dataset demonstrate significant reductions in End-Point-Error and D1-all%, validating its practical advantages in complex environments.

Real-Time Semantic Stereo Matching

In the paper titled "Real-Time Semantic Stereo Matching" by Pier Luigi Dovesi et al., the authors present a novel framework designed to improve the efficiency of autonomous systems through enhanced scene understanding. The proposed system aims to solve two fundamental tasks in computer vision: depth estimation and semantic segmentation, which are crucial for applications in robotics, self-navigation, and augmented reality.

The paper describes a compact and lightweight architecture called RTS$^2$Net, designed for real-time semantic stereo matching. This architecture is based on a coarse-to-fine multi-stage paradigm that enables fast inference while maintaining high accuracy comparable to state-of-the-art systems. The framework integrates both depth and semantic information extraction into a single, streamlined process, thus allowing for optimal trade-offs between speed and precision depending on application requirements.

Architecture Overview

RTS$^2$Net is engineered to exploit the complementary nature of depth and semantic information. The network consists of four key components:

• Shared Encoder: Extracts features from stereo image pairs. This forms the basis for both depth and semantic estimation.
• Stereo Disparity Decoder: Processes features to generate disparity maps at multiple resolutions for accurate depth perception.
• Semantic Decoder: Extracts contextual semantic information to classify pixels at various scales.
• Synergy Disparity Refinement Module: Combines the outputs from depth and semantic decoding to refine disparity estimates using semantic clues.

The synergistic relationship between depth estimation and semantic segmentation improves overall performance, particularly in complex scenes where certain objects (e.g., vegetation, reflective surfaces) can introduce ambiguity in depth perception.

Experimental Results

The authors validate RTS$^2$Net across several configurations, showing significant improvements over existing frameworks, especially in applications with stringent real-time requirements:

• On the KITTI 2015 validation split, RTS$^2$Net achieves substantial reductions in End-Point-Error (EPE) and D1-all%, outperforming standalone models.
• Even with limited computational resources, such as an NVIDIA Jetson TX2, RTS$^2$Net successfully maintains high-speed operation (up to several frames per second) with acceptable accuracy trade-offs.

The network's lightweight design ensures high performance on both high-end GPUs and embedded systems, highlighting its versatility across different platforms and application contexts.

Practical Implications and Future Work

RTS$^2$Net's capability to balance speed and precision makes it an excellent choice for deployment in real-world scenarios requiring immediate processing of visual input for decision-making tasks. Its efficiency and accuracy suggest potential applications in autonomous vehicle navigation, real-time mapping, and robotic task execution.

Future work could explore the extension of this architecture to more complex multi-task setups, including optical flow estimation and object tracking, broadening its applicability in dynamic environments. Additionally, research into further optimizing the trade-off mechanisms within multistage inference frameworks holds promise in achieving even lower latency without compromising accuracy.

In conclusion, RTS$^2$Net represents a significant advancement in semantic stereo matching, demonstrating that thoughtful integration of multiple vision tasks can yield systems that are both highly effective and practical for real-time applications.