Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

• URL: http://arxiv.org/abs/2605.21472v1
• Date: Wed, 20 May 2026 17:55:16 GMT
• Title: Stream3D: Sequential Multi-View 3D Generation via Evidential Memory
• Abstract summary: Stream3D is a training-free streaming mechanism that turns a frozen view-conditioned 3D generator into a streaming generator with constant cross-chunk memory.<n> evaluated on both realistic and synthetic streaming benchmarks.
• Score: 20.313446116978586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: View-conditioned 3D generators such as SAM 3D, TRELLIS and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visual observation often arrives as long monocular streams. Naively applying these generators to each streaming frame independently leads to severe temporal inconsistency in the generated results. To address this problem, we propose Stream3D, the first training-free streaming mechanism that turns a frozen view-conditioned 3D generator into a streaming generator with constant cross-chunk memory. Stream3D achieves this by maintaining a compact evidential memory, which selectively caches the most informative historical frames based on a proposed evidence score mechanism. As the stream progresses, the memory dynamically updates to retain a fixed number of informative frames, preventing the memory footprint from growing linearly with sequence length. This also prevents degradation over long sequences and keeps the underlying generator completely unchanged without retraining, architectural modifications, or auxiliary losses. Evaluated on both realistic and synthetic streaming benchmarks, Stream3D outperforms latent-transport baselines, including KV-cache reuse and flow-based feature editing, across both photometric and geometric metrics. More details can be found at: https://anonymous-submission-20.github.io/streaming3D.github.io/.

Related papers

• StreamSplat: Streaming Feed-Forward 3D Gaussian Splatting [13.605490241534797]
  StreamSplat is a feed-forward 3DGS framework that incrementally maintains a persistent geometry-grounded scene state.<n>Experiments show that StreamSplat remains competitive with state-of-the-art feed-forward 3DGS methods under sparse causal inputs.<n>StreamSplat scales to long input streams with 256, 512, and 1024 views where fixed-view baselines run out of memory.
  arXiv  Detail & Related papers  (2026-08-03T03:48:20Z)
• Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors [54.51965288351254]
  We present an online 3D vision-language model that enables real-time spatial understanding from streaming video.<n>Our approach adopts an autoregressive streaming control modeling based on the LLM's next-token prediction to learn when to respond.<n>To address the scarcity of streaming 3D-language data, we further develop a scalable data generation pipeline.
  arXiv  Detail & Related papers  (2026-06-05T04:16:24Z)
• Geometric Context Transformer for Streaming 3D Reconstruction [51.19524805829903]
  LingBot-Map is a feed-forward 3D foundation model for reconstructing scenes from streaming data.<n>A defining aspect of LingBot-Map lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory.<n>This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around 20 FPS on 518 x 378 resolution inputs.
  arXiv  Detail & Related papers  (2026-04-15T17:58:13Z)
• Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction [50.5449251266956]
  This paper addresses the task of large-scale 3D scene reconstruction from long video sequences.<n>Recent feed-forward reconstruction models have shown promising results by directly regressing 3D geometry from RGB images without explicit 3D priors or geometric constraints.<n>We propose a novel neural global context representation that efficiently compresses and retains long-range scene information.
  arXiv  Detail & Related papers  (2026-04-09T17:59:50Z)
• Mem3R: Streaming 3D Reconstruction with Hybrid Memory via Test-Time Training [8.037339305506656]
  We propose Mem3R, a streaming 3D reconstruction model with a hybrid memory design.<n>For camera tracking, Mem3R employs an implicit fast-weight memory implemented as a lightweight Multi-Layer Perceptron.<n>For geometric mapping, Mem3R maintains an explicit token-based fixed-size state.
  arXiv  Detail & Related papers  (2026-04-08T16:41:22Z)
• I3DM: Implicit 3D-aware Memory Retrieval and Injection for Consistent Video Scene Generation [56.33710337846449]
  I3DM is a novel implicit 3D-aware memory mechanism for consistent video scene generation.<n>At the core of our approach is a 3D-aware memory retrieval strategy.<n>To fully utilize the retrieved historical frames, we introduce a 3D-aligned memory injection module.
  arXiv  Detail & Related papers  (2026-03-24T16:45:40Z)
• STAC: Plug-and-Play Spatio-Temporal Aware Cache Compression for Streaming 3D Reconstruction [16.772404929988507]
  3D reconstruction from streaming inputs requires both long-term temporal consistency and efficient memory usage.<n> STAC is a framework for streaming 3D reconstruction with large causal transformers.<n> experiments show that STAC achieves state-of-the-art reconstruction quality while reducing memory consumption by nearly 10x and accelerating inference by VG 4x.
  arXiv  Detail & Related papers  (2026-03-18T06:36:46Z)
• STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer [72.88105562624838]
  We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem.<n>By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios.<n>Our results underscore the potential of causal Transformer models for online 3D perception, paving the way for real-time 3D understanding in streaming environments.
  arXiv  Detail & Related papers  (2025-08-14T17:58:05Z)
• LONG3R: Long Sequence Streaming 3D Reconstruction [29.79885827038617]
  Long3R is a novel model designed for streaming multi-view 3D scene reconstruction over longer sequences.<n>Our model achieves real-time processing by operating recurrently, maintaining and updating memory with each new observation.<n>Experiments demonstrate that LONG3R outperforms state-of-the-art streaming methods, particularly for longer sequences.
  arXiv  Detail & Related papers  (2025-07-24T09:55:20Z)
• Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory [72.75478398447396]
  We propose Point3R, an online framework targeting dense streaming 3D reconstruction.<n>To be specific, we maintain an explicit spatial pointer memory directly associated with the 3D structure of the current scene.<n>Our method achieves competitive or state-of-the-art performance on various tasks with low training costs.
  arXiv  Detail & Related papers  (2025-07-03T17:59:56Z)
• SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder and Transformer Network [1.4732811715354455]
  We introduce a novel approach for 3D human action recognition, denoted as SpATr (Spiral Auto-encoder and Transformer Network) A lightweight auto-encoder, based on spiral convolutions, is employed to extract spatial geometrical features from each 3D mesh. The proposed method is evaluated on three prominent 3D human action datasets: Babel, MoVi, and BMLrub.
  arXiv  Detail & Related papers  (2023-06-30T11:49:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.