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Volumetric Environment Representation for Vision-Language Navigation
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Proposes Volumetric Environment Representation (VER), which voxelizes the scene into 3D cells built from multi-view 2D features via 2D-3D sampling, trained with multi-task 3D perception, to drive a volume-state and episodic-memory VLN agent.
Problem
Vision-language navigation agents build environment representations from 2D perspective features, which lose 3D geometry and semantics and yield incomplete scene understanding for navigation decisions.
Approach
Voxelize the environment into structured 3D cells, aggregate multi-view ViT features into them with cross-view deformable attention, extract them coarse-to-fine with 3D deconvolutions under multi-task supervision (3D occupancy, room layout, 3D boxes), and use the resulting volume state plus episodic topological memory for action prediction.
Key results
- On R2R test unseen, the model reaches 76% SR and 66% SPL, about 3% SR and 4% SPL over BEVBert.
- On REVERIE val unseen, SR and SPL improve by 3.86% and 4.07% over BSG.
- On 3D tasks (val unseen), occupancy IoU 24.31 vs 22.12 for OccNet and detection mAP 33.57 vs 29.91.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use VER instead of BEVBert | R2R navigation · R2R test unseen | SR: 73 -> 76 | Helped |
| use VER instead of BSG | REVERIE navigation · REVERIE val unseen | SR: 52.12 -> 55.98 | Helped |
| add volume state estimation (ablation) | VER navigation · R2R val unseen | SR: 72.71 -> 75.80 | Helped |
| add episodic memory (ablation) | VER navigation · REVERIE val unseen | RGS: 31.36 -> 33.71 | Helped |
| set neighborhood range |Ωn| to 4 instead of 9 (ablation) | VER navigation · R2R val unseen | SR: 75.80 -> 73.75 | Hurt |
| set neighborhood range |Ωn| to 16 instead of 9 (ablation) | VER navigation · REVERIE val unseen | SR: 55.98 -> 52.18 | Hurt |
| replace learnable 3D deconvolution up-sampling with trilinear interpolation (ablation) | VER representation extraction · 3D occupancy prediction | mIoU: 12.93 -> 11.03 | Hurt |
| add coarse-to-fine extraction (ablation) | VER representation extraction · 3D occupancy prediction | mIoU: 12.39 -> 12.93 | Helped |
| add object detection training to occupancy prediction (ablation) | VER representation learning · 3D occupancy prediction | mIoU: 12.09 -> 12.14 | Helped |
| add room layout estimation to occupancy and object detection training (ablation) | VER representation learning · 3D object detection | mAP: 32.64 -> 33.57 | Helped |
| add room layout estimation to occupancy prediction training (ablation) | VER representation learning · 3D occupancy prediction | mIoU: 12.09 -> 11.37 | Hurt |
Limitations
- Annotations for 3D tasks are generated from LiDAR labels on Matterport3D, which requires extra annotation pipeline effort.
- Ablation shows performance sensitive to neighborhood range, with too large range hurting results.
Experimental setup
Datasets: R2R REVERIE R4R Matterport3D
Benchmarks: R2R REVERIE R4R
Metrics: SR SPL TL NE OSR RGS RGSPL CLS nDTW SDTW
Baselines: BEVBert DUET BSG LANA HAMT BEVFormer OccNet TD-STP
Builds on: BEVBert DUET HAMT BEVFormer OccNet
Closest papers by meaning
- Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation (NeurIPS 2025 · similarity 0.90)
- GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation (CVPR 2026 · similarity 0.89)
- D3D-VLP: Dynamic 3D Vision-Language-Planning Model for Embodied Grounding and Navigation (CVPR 2026 · similarity 0.87)
- Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation (NeurIPS 2025 · similarity 0.87)
- g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks (CVPR 2025 · similarity 0.87)
- VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction (CVPR 2026 · similarity 0.87)
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