Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
years
2026 7roles
background 3polarities
background 3representative citing papers
MarineSTD-GS disentangles true underwater scene appearance from video degradations by deriving degraded Gaussian colors from paired intrinsic Gaussians via a physical spatiotemporal model.
A satellite-free training framework reconstructs 3D drone scenes via Gaussian splatting, generates geometry-normalized pseudo-orthophotos, and aggregates DINOv3 features with a Fisher vector model trained only on drone data to enable cross-view retrieval.
The paper proposes ray-aware pointer memory with adaptive retain-or-replace updates to improve long-term stability and pose accuracy in streaming 3D reconstruction.
MemoryDiorama generates animated 3D dioramas from photos via LLM scene analysis and generative components, yielding richer autobiographical recall than photo-only or static diorama baselines.
CausalGS decouples scene kinematics and dynamics from videos via inverse physics inference on Gaussian representations and guides learning with a differentiable simulator to achieve better long-term future frame prediction.
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.
citing papers explorer
-
Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes
Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
-
Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction
MarineSTD-GS disentangles true underwater scene appearance from video degradations by deriving degraded Gaussian colors from paired intrinsic Gaussians via a physical spatiotemporal model.
-
Satellite-Free Training for Drone-View Geo-Localization
A satellite-free training framework reconstructs 3D drone scenes via Gaussian splatting, generates geometry-normalized pseudo-orthophotos, and aggregates DINOv3 features with a Fisher vector model trained only on drone data to enable cross-view retrieval.
-
Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction
The paper proposes ray-aware pointer memory with adaptive retain-or-replace updates to improve long-term stability and pose accuracy in streaming 3D reconstruction.
-
MemoryDiorama: Generating Dynamic 3D Diorama from Everyday Photos for Memory Recall
MemoryDiorama generates animated 3D dioramas from photos via LLM scene analysis and generative components, yielding richer autobiographical recall than photo-only or static diorama baselines.
-
CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations
CausalGS decouples scene kinematics and dynamics from videos via inverse physics inference on Gaussian representations and guides learning with a differentiable simulator to achieve better long-term future frame prediction.
-
AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.