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DepthSplat: Connecting Gaussian Splatting and Depth
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Gaussian splatting and single-view depth estimation are typically studied in isolation. In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and study their interactions. More specifically, we first contribute a robust multi-view depth model by leveraging pre-trained monocular depth features, leading to high-quality feed-forward 3D Gaussian splatting reconstructions. We also show that Gaussian splatting can serve as an unsupervised pre-training objective for learning powerful depth models from large-scale multi-view posed datasets. We validate the synergy between Gaussian splatting and depth estimation through extensive ablation and cross-task transfer experiments. Our DepthSplat achieves state-of-the-art performance on ScanNet, RealEstate10K and DL3DV datasets in terms of both depth estimation and novel view synthesis, demonstrating the mutual benefits of connecting both tasks. In addition, DepthSplat enables feed-forward reconstruction from 12 input views (512x960 resolutions) in 0.6 seconds.
Forward citations
Cited by 9 Pith papers
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TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation
TinySplat compresses feedforward 3D Gaussian scenes by 105-199x on two-view benchmarks (about 50x on DL3DV) while keeping rendered quality close to the uncompressed model.
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LongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images
A feed-forward 3D Gaussian Splatting pipeline that incrementally fuses and compresses historical Gaussians using a 2D image-like representation.
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RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration
RegGS aligns locally generated 3D Gaussian maps using a Sinkhorn-approximated mixture Wasserstein distance, improving pose estimation and novel view synthesis from sparse unposed views.
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JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splatting
A feed-forward 3D Gaussian splatting method fuses depth and optical flow via a learned reliability mask, improving novel-view PSNR on RealEstate10K by 0.19 dB over its backbone.
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RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes
RadarSplat brings Gaussian Splatting to automotive radar, explicitly modeling multipath and receiver noise to synthesize realistic radar images and estimate occupancy.
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MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models
A feed-forward architecture that reuses a frozen depth foundation model to predict 3D Gaussian primitives, improving novel view synthesis and cross-dataset generalization.
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Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features
A video-to-4D generation method that decouples dynamic and static features in DINOv2 space and fuses similar dynamic information across views reports state-of-the-art scores on Consistent4D and Objaverse.
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VistaVLA: Geometry- and Semantic-Aware 3D Gaussian-Grounded VLA for Robotic Manipulation
VistaVLA lifts multi-view vision-language features into 3D Gaussians, compresses them 99% via Merge-then-Query, and improves real-robot manipulation success by ~23% over baselines.
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FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation
FVGen uses GAN-based adversarial distillation and softened reverse KL divergence to compress a video diffusion teacher for novel-view synthesis into a four-step student with comparable quality.
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