AdaptSplat adds a Frequency-Preserving Adapter to vision foundation models to boost high-frequency fidelity and cross-domain performance in feed-forward 3D Gaussian Splatting.
arXiv preprint arXiv:2512.10950 (2025) 3
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 6representative citing papers
Video diffusion models can be adapted into permutation-invariant generators for sparse novel view synthesis by treating the problem as video completion and removing temporal order cues.
Anchoring Gaussian centers to predicted raymaps and jointly optimizing RGB, raymap, and camera losses with a dual-frequency curriculum suppresses pose drift and improves pose-free 3D reconstruction on long sequences.
BEAST3D learns viewpoint-invariant 3D features from calibrated multi-view animal videos via Gaussian splatting for novel view synthesis, pose estimation, and neural encoding across four species.
SelfEvo enables pretrained 4D perception models to self-improve on unlabeled videos via self-distillation, delivering up to 36.5% relative gains in video depth estimation and 20.1% in camera estimation across eight benchmarks.
Decoder-only view synthesis model using KV-cache representation and weight sharing between reconstruction and rendering networks achieves new SOTA on novel view synthesis benchmarks.
citing papers explorer
-
AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting
AdaptSplat adds a Frequency-Preserving Adapter to vision foundation models to boost high-frequency fidelity and cross-domain performance in feed-forward 3D Gaussian Splatting.
-
Novel View Synthesis as Video Completion
Video diffusion models can be adapted into permutation-invariant generators for sparse novel view synthesis by treating the problem as video completion and removing temporal order cues.
-
NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction
Anchoring Gaussian centers to predicted raymaps and jointly optimizing RGB, raymap, and camera losses with a dual-frequency curriculum suppresses pose drift and improves pose-free 3D reconstruction on long sequences.
-
BEAST3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting
BEAST3D learns viewpoint-invariant 3D features from calibrated multi-view animal videos via Gaussian splatting for novel view synthesis, pose estimation, and neural encoding across four species.
-
Self-Improving 4D Perception via Self-Distillation
SelfEvo enables pretrained 4D perception models to self-improve on unlabeled videos via self-distillation, delivering up to 36.5% relative gains in video depth estimation and 20.1% in camera estimation across eight benchmarks.
-
DVSM: Decoder-only View Synthesis Model Done Right
Decoder-only view synthesis model using KV-cache representation and weight sharing between reconstruction and rendering networks achieves new SOTA on novel view synthesis benchmarks.