Open Problems

3D Gaussian Splatting & Novel View Synthesis

Robust Wide-Baseline 3D Gaussian Splatting Under Unreliable Foundation-Model Geometry Priors

Barrier to removeOpen
Weak candidate · 1/3 runs3 papers report this100% from 2025+

Generated automatically from the limitations stated in 3 papers (ICLR, NeurIPS, ICCV), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Modern feed-forward 3D Gaussian Splatting pipelines rely on pairwise foundation models (such as MASt3R or VGGT) to supply initial coordinate pointmaps and relative camera poses. Because these foundation predictors degrade significantly as angular view differences increase and inject persistent geometric noise, downstream view synthesis fails when views are not captured in dense, small-step sequences. Treating these imperfect upstream predictions as rigid pseudo-ground truth structurally blocks feed-forward novel view synthesis on sparse, wide-baseline image collections.

Why it matters

Enables high-fidelity 3D Gaussian scene reconstruction from unposed, sparse, wide-baseline image sets without requiring slow classical Structure-from-Motion or continuous video trajectories.

Ways to approach it

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  1. 1

    Joint photometric and geometry uncertainty re-weighting: Implement an uncertainty-guided Gaussian densification scheme that weights MASt3R pointmaps and pose priors based on visual foundation model confidence and epipolar residuals; measure PSNR, SSIM, and geometric floater artifacts across baseline angles ranging from 15° to 90° on CO3D.

  2. 2

    Differentiable rendering-loss pose refinement: Build a joint optimization loop where 3D Gaussian positions and upstream coarse camera poses are continuously refined against multi-view photometric consistency; measure camera rotation/translation error and novel view synthesis quality against COLMAP baselines.

  3. 3

    Cycle-consistent graph rectification for foundation poses: Construct a relative pose graph from all pairwise foundation model outputs and solve for synchronized global camera trajectories prior to Gaussian initialization; measure pose accuracy and reconstruction completeness on sparse-view benchmarks.

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Why it might fail

Rapid architectural scaling in next-generation multi-view foundation models could resolve wide-baseline pose regression and depth estimation natively, dissolving the need for downstream pose-and-geometry correction within the Gaussian splatting pipeline.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

3D Gaussian Splatting & Novel View Synthesis

Scope to testOpen

Cross-Domain Robustness and Generalization Benchmarking for 3D Gaussian Splatting

Novel view synthesis and 3D Gaussian Splatting methods are currently evaluated almost exclusively on room-scale indoor datasets such as Replica, ScanNet++, and Gibson. Because no single pipeline has been systematically evaluated across indoor, outdoor unbounded, panoramic, and object-centric domains, the robustness and failure modes of these representations under domain shift remain entirely unknown. Practitioners and researchers cannot determine whether observed performance gains are artifacts of bounded indoor geometries or if current densification and optimization heuristics translate to unconstrained environments.

Possible candidate · 2/5 runs7 papers report this71% from 2025+

3D Gaussian Splatting & Novel View Synthesis

Effect to explainPartly addressed

Dynamic 3D Gaussian Splatting Under Non-Smooth and Discontinuous Motion Dynamics

Current dynamic novel view synthesis and 4D Gaussian Splatting frameworks assume smooth, continuous spatio-temporal deformations, linear velocities, and fixed temporal windows. When scenes exhibit abrupt impacts, rapid accelerations, chaotic object trajectories, or zooming and variable frame rates, these continuous deformation priors fail and yield severe geometric tearing, blurring, or total tracking collapse. This fundamentally blocks dynamic radiance fields from capturing real-world interactions such as sports action, mechanical impacts, and sudden dynamic occlusions.

Possible candidate · 3/5 runs4 papers report this75% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.