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Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review

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arxiv 2402.11141 v1 pith:6A6NPX4O submitted 2024-02-17 cs.CV

Thang-Anh-Quan Nguyen , Amine Bourki , Mátyás Macudzinski , Anthony Brunel , Mohammed Bennamoun This is my paper
classification cs.CV
keywords scenefieldsneuralradiancesemantically-awarevisualcomprehensivelabels
verification ladder T0 review T1 audit T2 compute T3 formal
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This review thoroughly examines the role of semantically-aware Neural Radiance Fields (NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It explores how NeRFs adeptly infer 3D representations for both stationary and dynamic objects in a scene. This capability is pivotal for generating high-quality new viewpoints, completing missing scene details (inpainting), conducting comprehensive scene segmentation (panoptic segmentation), predicting 3D bounding boxes, editing 3D scenes, and extracting object-centric 3D models. A significant aspect of this study is the application of semantic labels as viewpoint-invariant functions, which effectively map spatial coordinates to a spectrum of semantic labels, thus facilitating the recognition of distinct objects within the scene. Overall, this survey highlights the progression and diverse applications of semantically-aware neural radiance fields in the context of visual scene interpretation.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Active Learning for Label-Efficient Training of Semantic Neural Radiance Field

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Active learning with a 3D spatial-diversity term cuts annotation cost by over 2x for semantically-aware NeRF training versus random sampling.

  2. OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness

    cs.CV 2025-06 conditional novelty 6.0 of 10

    OC-SOP fuses object detection queries into a semantic occupancy completion U-Net, improving foreground-object voxel accuracy and achieving state-of-the-art mIoU on SemanticKITTI.

  3. EscherNet++: Simultaneous Amodal Completion and Scalable View Synthesis through Masked Fine-Tuning and Enhanced Feed-Forward 3D Reconstruction

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A masked fine-tuned diffusion model simultaneously completes occluded views and synthesizes novel viewpoints, enabling fast feed-forward 3D reconstruction.

  4. RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    RAZER fuses online TSDF reconstruction with open-vocabulary instance embeddings and tracking to produce real-time, queryable 3D semantic maps without any 3D training.

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