In-Place Scene Labelling and Understanding with Implicit Scene Representation (2103.15875v2)

Abstract: Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes. Recent implicit neural reconstruction techniques are appealing as they do not require prior training data, but the same fully self-supervised approach is not possible for semantics because labels are human-defined properties. We extend neural radiance fields (NeRF) to jointly encode semantics with appearance and geometry, so that complete and accurate 2D semantic labels can be achieved using a small amount of in-place annotations specific to the scene. The intrinsic multi-view consistency and smoothness of NeRF benefit semantics by enabling sparse labels to efficiently propagate. We show the benefit of this approach when labels are either sparse or very noisy in room-scale scenes. We demonstrate its advantageous properties in various interesting applications such as an efficient scene labelling tool, novel semantic view synthesis, label denoising, super-resolution, label interpolation and multi-view semantic label fusion in visual semantic mapping systems.

• The paper introduces Semantic-NeRF, which extends neural radiance fields to jointly encode geometry, appearance, and semantics for precise scene labelling.
• It employs a multi-layer perceptron to map 3D positions and viewing directions to semantic logits, effectively propagating sparse annotations.
• Results on Replica and ScanNet datasets show the method maintains high semantic accuracy using as little as 10% labelled data, matching fully supervised benchmarks.

In-Place Scene Labelling and Understanding with Implicit Scene Representation

This paper presents an advancement in the domain of 3D scene understanding through the integration of semantic labelling with implicit scene representations. The authors propose an extension to neural radiance fields (NeRF), known as Semantic-NeRF, which jointly encodes semantics, appearance, and geometry. This hybrid approach demonstrates the potential to accurately generate 2D semantic labels while employing minimal in-place annotations, showcasing its effectiveness in environments where data may be sparse or noisy.

Technical Contributions

Semantic-NeRF leverages the strengths of NeRF, which has been effectively utilized for tasks such as realistic view synthesis, to incorporate semantic segmentation. By training on images specific to a scene, Semantic-NeRF can produce implicit 3D representations that facilitate the generation of accurate semantic labels.

The paper's technical innovation lies in mapping both the positions and viewing directions to semantic logits using a multi-layer perceptron (MLP) architecture. The semantic logits are then projected back into the 2D space, enabling effective scene labelling. This method proves to be resilient, sustaining performance even when only sparse or noisy training annotations are available. The learning process inherently integrates multi-view consistency, which helps propagate sparse annotations efficiently.

Quantitative and Qualitative Results

The empirical studies involve the Replica and ScanNet datasets, where Semantic-NeRF is evaluated under conditions of sparse, noisy, and partial labels. One key finding is the network's capacity to maintain semantic accuracy with as low as 10% of the labelled data, suggesting that Semantic-NeRF is highly efficient in label propagation. Quantitative comparisons showed comparable performance with fully supervised benchmarks, further validating its robustness to noisy and partial data.

Qualitative analyses reinforce the quantitative results by illustrating coherent semantic renderings even under significant label noise or low-resolution scenarios. Semantic-NeRF further demonstrates the ability to fuse multi-view semantic labels, outperforming other fusion techniques that rely heavily on depth maps. The potential to correct pixel-level or region-level errors through this fusion indicates its application in real-world scenarios, such as robotics, where quick adaptation to new scenes is critical.

Theoretical and Practical Implications

The dual focus on geometry and semantics opens new avenues for scene understanding in AI, underscoring the potential for self-supervision in open-set scenarios. By bypassing the reliance on large, annotated datasets, this approach may simplify scene labelling processes and enable more rapid deployment across different environments. The framework also holds promise for interactive labelling applications, where the user can provide minimal inputs that the model amplifies to generate comprehensive semantic maps.

Future research can expand upon this architecture by exploring more refined positional encodings or integrating more robust multi-view geometry constraints. The combined implicit and explicit 3D representations can be further leveraged to develop applications like interactive scene exploration or augmented reality, where intricate scene details must be accurately represented with minimal overhead.

In conclusion, this work stands as a significant step towards versatile and efficient semantic understanding in static scenes, demonstrating how implicit scene representations can be enriched to provide significant utility in practical AI applications.