SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations

Abstract: Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities. However, while there has been much work on the correct formulation for geometrical estimation, state-of-the-art systems usually rely on simple semantic representations which store and update independent label estimates for each surface element (depth pixels, surfels, or voxels). Spatial correlation is discarded, and fused label maps are incoherent and noisy. We introduce a new compact and optimisable semantic representation by training a variational auto-encoder that is conditioned on a colour image. Using this learned latent space, we can tackle semantic label fusion by jointly optimising the low-dimenional codes associated with each of a set of overlapping images, producing consistent fused label maps which preserve spatial correlation. We also show how this approach can be used within a monocular keyframe based semantic mapping system where a similar code approach is used for geometry. The probabilistic formulation allows a flexible formulation where we can jointly estimate motion, geometry and semantics in a unified optimisation.

• The paper introduces a learned latent space that enables joint optimization of semantic labels across overlapping views.
• It leverages a conditional variational auto-encoder and a U-shaped network architecture for integrated depth and semantic segmentation.
• The approach significantly enhances dense semantic reconstruction, paving the way for real-time applications in robotic navigation and manipulation.

SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations

The paper "SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations" offers a sophisticated approach to monocular semantic mapping in robotics, aimed at overcoming the limitations of existing real-time scene understanding systems. The authors propose an optimizable, compact semantic representation achieved through a variational auto-encoder (VAE) conditioned on color images. This advanced framework allows for the creation of spatially coherent and consistent semantic label maps.

A key contribution of this paper is the introduction of a learned latent space that facilitates the fusion of semantic labels through joint optimization of low-dimensional codes across overlapping image sets. This results in label maps with preserved spatial correlations—an improvement over previous methods where each surface element independently stores and updates class labels. The proposed method emphasizes the use of a monocular keyframe-based semantic mapping system that employs a similar encoding approach for geometry, thus enabling the joint consideration of motion, geometry, and semantic labels in a unified optimization process.

Several findings underscore the efficacy of the approach. The method improves the fusion of semantic labels by optimizing for consistency across multiple views, using learned codes as well as introducing a latent space conditioned on image data. The authors highlight the enhanced geometric and semantic representation achievable through compact learned codes, derived by training a multitask conditional VAE (CVAE). By encapsulating multimodal distributions of semantic segmentation, the CVAE not only enhances semantic consistency across views but also aids in refining geometric structures through dense semantic structure optimization.

The methodological innovations include the combination of a U-shaped network architecture for semantic processing and two separate VAEs for depth and semantic segmentation, both conditioned on image data. Furthermore, the multi-view label fusion method proposed in the paper shows remarkable improvements in semantic label accuracy over traditional element-wise methods, as evidenced by quantitative and qualitative results.

Further experiments demonstrate the potential of this system in applications such as dense monocular semantic SLAM. This is an essential contribution, offering a refined system that supports real-time operations and improved spatial AI capabilities for real-world robotic systems. The potential applications in autonomous navigation and robotic manipulation demonstrate the significant impact of such advancements.

In conclusion, this paper addresses key challenges in semantic mapping by leveraging deep learning frameworks for compact representation and code optimization. Future work could focus on expanding the unification of geometric and semantic representations, thus continuing progress toward efficient and robust scene models. The research represents a significant step toward rendering robotic systems capable of intuitive spatial and semantic reasoning akin to human perception.