In-Place Scene Labelling and Understanding with Implicit Scene Representation

The evolution of scene understanding has transitioned from explicit geometric representations like point clouds and voxels to implicit neural models such as NeRF, which offer continuous, high-fidelity scene reconstructions. Prior works like PointNet, VoxelNet, and DeepSDF have advanced geometric modeling, but integrating semantics remains challenging due to annotation costs and data scarcity. Recent efforts combine multi-task learning and pretraining, yet they often require extensive labeled datasets. Neural implicit representations, especially NeRF, have demonstrated remarkable view synthesis capabilities but lack inherent semantic understanding. This work builds upon these foundations, aiming to embed semantic information directly into the scene representation, leveraging multi-view consistency to mitigate annotation limitations.

The core challenge is achieving dense, accurate semantic labeling of complex scenes with minimal supervision. Existing methods depend heavily on large annotated datasets, which are costly and labor-intensive. Noisy or sparse labels further degrade performance, especially in real-world scenarios where perfect annotations are impractical. The difficulty lies in propagating limited labels across multiple views while maintaining semantic coherence and detail. Addressing this bottleneck is crucial for deploying scene understanding in autonomous systems, virtual reality, and large-scale mapping, where annotation costs and environmental variability pose significant hurdles.

The key innovations include: 1) Extending NeRF to jointly encode geometry, appearance, and semantics, enabling end-to-end scene modeling; 2) Introducing a semantic rendering branch that predicts class logits, facilitating multi-view consistent label propagation; 3) Developing a robust training scheme that leverages sparse and noisy labels, supported by multi-view geometric regularization; 4) Enabling super-resolution and label interpolation, which reconstruct high-detail semantic maps from minimal supervision. These innovations collectively reduce annotation burdens, improve robustness, and enhance scene understanding capabilities beyond prior explicit or pre-trained models.

• �� Input: Multiple RGB images with known camera poses.
• �� Base: NeRF employs an MLP with positional encoding to predict volumetric density and color.
• �� Extension: Adds a semantic branch predicting logits s(x), shared with density prediction.
• �� Training: Minimizes combined photometric loss (L_p) and semantic cross-entropy loss (L_s), balancing via λ.
• �� Multi-view consistency: Uses the inherent geometric regularization of NeRF to propagate sparse labels.
• �� Super-resolution: Trains on low-resolution or sparse labels, re-rendering high-resolution semantic maps.
• �� Label interpolation: Uses the model to fill in missing semantic details.
• �� Fusion: Integrates multiple noisy or partial labels into a coherent 3D semantic map.
• �� Implementation: Uses hierarchical sampling, positional encoding, and Adam optimizer, trained on GPU for 200k iterations.

The experiments utilize Replica and ScanNet datasets, with scenes rendered at 640x480 resolution. Sparse labels are simulated by sampling a small fraction of frames (less than 10%), with noise added to test robustness. The model's performance is evaluated using metrics like mIoU, accuracy, and entropy, comparing against baselines. Ablation studies analyze the impact of label sparsity, noise, super-resolution, and multi-view fusion. Hyperparameters such as learning rate, number of iterations, and λ are tuned for optimal results. The experiments demonstrate the model's capacity to learn dense semantic maps from minimal supervision, with qualitative visualizations supporting quantitative metrics.

Results show that with less than 10% labeled frames, the model achieves over 95% mIoU, outperforming traditional methods significantly. Super-resolution experiments demonstrate 8-16x enhancement in semantic detail, validating the effectiveness of label interpolation. The model maintains high accuracy and low entropy in noisy label scenarios, effectively denoising and refining semantic maps. Multi-view fusion further improves label consistency, reducing errors caused by sparse or noisy annotations. These findings confirm the approach's robustness and practical utility for real-world scene understanding.

The method can be directly applied to robotic navigation, indoor scene mapping, and augmented reality, where manual annotation is costly. It enables dense semantic labeling with minimal supervision, reducing labor costs and increasing scalability. Future applications include dynamic scene modeling, real-time semantic updates, and integration with SLAM systems, facilitating autonomous exploration and virtual environment generation. The approach's robustness to noise and sparse labels makes it suitable for deployment in diverse, real-world scenarios.

The current framework assumes static scenes with accurate camera poses; dynamic environments require further adaptation. Computational costs remain high, limiting real-time deployment. Handling extreme label noise or severely incomplete annotations can still pose challenges. Additionally, outdoor large-scale scenes and dynamic objects are not yet addressed, requiring future research to extend robustness and efficiency.