RTGS: Real-Time 3D Gaussian Splatting SLAM via Multi-Level Redundancy Reduction

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Review Form:

Reviewer: The Guardian (Adversarial Skeptic)

Summary

The authors present RTGS, a co-designed algorithm and hardware framework to accelerate 3D Gaussian Splatting-based SLAM systems for real-time performance on edge devices. The core thesis is that significant redundancies exist at multiple levels of the 3DGS-SLAM pipeline (Gaussian, pixel, workload, memory access). The authors propose algorithmic solutions (adaptive pruning, dynamic downsampling) and a hardware plug-in architecture (featuring a Workload Scheduling Unit, R&B Buffer, and Gradient Merging Unit) to address these redundancies. The system is evaluated against several 3DGS-SLAM algorithms and datasets, claiming real-time performance (≥30 FPS) with "negligible quality loss."

Strengths

1. Comprehensive Scope: The work commendably attempts to address performance bottlenecks across the entire 3DGS-SLAM pipeline, from algorithm to hardware architecture. This multi-level approach is ambitious.
2. Hardware-Level Optimizations: The proposed R&B Buffer for reusing intermediate rendering data during backpropagation is a clever and well-justified optimization. Similarly, the design of the Gradient Merging Unit (GMU) to handle sparse gradient aggregation appears to be a technically sound approach to mitigating the known bottleneck of atomic operations.
3. Extensive Profiling: The authors have conducted detailed profiling (Section 3) to motivate their design choices. This analysis provides a useful, if not entirely novel, breakdown of where latency is concentrated in the 3DGS-SLAM pipeline.

Weaknesses

The paper's claims of robustness and efficiency rest on a foundation of questionable heuristics and an evaluation that lacks sufficient rigor.

1. Algorithmic Justification is Heuristic and Lacks Principled Analysis: The core algorithmic contributions are based on "magic numbers" and ad-hoc rules that are not adequately justified.

   • Adaptive Pruning (Section 4.1): The importance score in Eq. 7 uses a weighting factor λ, which is not defined or analyzed. The pruning interval K is adjusted based on a tile-Gaussian intersection change ratio exceeding a 5% threshold. Why 5%? This appears to be an empirically tuned value that may not generalize. A robust method should not rely on such arbitrary thresholds.
   • Dynamic Downsampling (Section 4.2): The resolution scaling for non-keyframes starts at (1/16)Ro and increases by a factor of m=2. The choice of these specific values is not justified. A sensitivity analysis is required to demonstrate that these are optimal choices and not simply values that worked for the selected test cases.

2. The "Negligible Quality Loss" Claim is Unsubstantiated and Contradicted by Data: The abstract makes a strong claim of "negligible quality loss," but the evidence is weak and, in some cases, contradictory.

   • The authors state in Section 4.2 that with their downsampling, "both ATE and PSNR remain within a 10% variance". A 10% degradation in Absolute Trajectory Error (ATE) is far from negligible in any serious robotics or AR/VR application and could represent a critical failure.
   • Table 6 presents several instances where ATE improves after applying the RTGS optimizations (e.g., GS-SLAM on ScanNet, ATE drops from 2.85 to 2.76). This is a highly counter-intuitive result. The authors provide no explanation for why removing information (pruning Gaussians, downsampling pixels) would lead to a more accurate trajectory. This suggests either an issue with the evaluation methodology or that the baseline implementations are suboptimal. Extraordinary claims require extraordinary evidence, which is absent here.
   • PSNR, the measure of rendering quality, consistently drops across all experiments in Table 6. While the drops are small, they are not zero, which again challenges the term "negligible."

3. The Pruning Strategy's Robustness is Questionable: The ablation study on pruning ratio (Figure 14a) reveals a critical weakness. The authors observe that ATE increases sharply beyond a 50% pruning ratio and therefore "cap the pruning ratio at 50%". This is not a strength but an admission that the proposed importance score (Eq. 7) is not a reliable measure of a Gaussian's true contribution. A truly robust importance metric would naturally preserve critical Gaussians even at high pruning ratios; the need for an external, arbitrary cap implies the metric is flawed.

4. Hardware Evaluation Oversimplifies Critical Aspects:

   • GPU Integration Model (Section 5.5): The proposed programming interface and synchronization via shared-memory flags is a high-level abstraction. It completely ignores the significant real-world overheads of polling, cache coherency traffic, and scheduler contention that would arise from such tight coupling between the SMs and an external accelerator. The performance model appears overly optimistic.
   • Ablation Study of Speedups (Figure 17b): The overall speedup is presented as a product of independent factors from each optimization. This assumes the contributions are orthogonal, which is highly unlikely. For instance, Adaptive Pruning reduces the total workload, which in turn affects the severity of workload imbalance that the WSU must solve. The speedup from the WSU is therefore dependent on the pruning ratio. The analysis does not account for these interactions, making the reported breakdown misleading.

Questions to Address In Rebuttal

1. Please provide a rigorous justification for the choice of hyperparameters in your algorithms: the pruning score weight λ, the 5% change ratio threshold for adapting K, and the (1/16) and m=2 constants for dynamic downsampling. A sensitivity analysis showing the impact of these parameters on both performance and accuracy is expected.
2. The claim of "negligible quality loss" requires stronger defense. Please specifically address: (a) Why a potential 10% increase in ATE should be considered negligible for SLAM applications. (b) The mechanism by which your method of removing information leads to improved ATE in several cases reported in Table 6. This counter-intuitive result must be explained.
3. If the proposed Gaussian importance score (Eq. 7) is robust, why is it necessary to enforce a hard 50% cap on the pruning ratio to prevent accuracy degradation? Does this not indicate a fundamental limitation of the metric itself?
4. How do you validate the assumption that the speedup contributions from your various hardware and software techniques (as shown in Figure 17b) are independent and can be multiplied? Please provide evidence that the speedup from one component (e.g., WSU) is not dependent on the operation of another (e.g., Adaptive Pruning).
5. What are the estimated latency and energy overheads of the synchronization mechanism between the GPU SMs and the RTGS plug-in? The current model seems to assume these are zero.

Review Form:

Reviewer: The Synthesizer (Contextual Analyst)

Summary

This paper presents RTGS, a holistic algorithm-hardware co-design framework aimed at enabling real-time performance for 3D Gaussian Splatting (3DGS) based SLAM systems on resource-constrained edge devices. The core problem addressed is the significant computational and memory overhead of existing 3DGS-SLAM methods, which prevents them from achieving the ≥30 FPS threshold required for interactive applications.

The authors' central contribution is a systematic, multi-level approach to identifying and eliminating redundancies throughout the SLAM pipeline. On the algorithm side, they introduce an adaptive Gaussian pruning method that reuses existing backpropagation gradients to identify unimportant Gaussians, and a dynamic down-sampling technique for non-keyframes that leverages the SLAM system's own keyframe identification logic. On the hardware side, they propose a dedicated GPU plug-in featuring several novel units: a Workload Scheduling Unit (WSU) to balance load across pixels, a Rendering and Backpropagation (R&B) Buffer to reuse intermediate data between pipeline stages, and a Gradient Merging Unit (GMU) to accelerate gradient aggregation without costly atomic operations.

By tackling inefficiencies at the object, pixel, execution, and pipeline levels simultaneously, RTGS demonstrates the ability to significantly accelerate existing 3DGS-SLAM algorithms, achieving real-time performance and substantial energy efficiency gains with negligible impact on accuracy.

Strengths

1. Holistic, System-Level Contribution: The most significant strength of this work is its comprehensive, system-level perspective. Rather than proposing a single point-solution, the authors have conducted a thorough analysis of the entire 3DGS-SLAM pipeline (Section 3, pages 3-5), identified multiple, distinct bottlenecks (Observations 1-6), and engineered a set of synergistic solutions. This algorithm-hardware co-design philosophy is powerful and leads to a much more impactful result than a purely algorithmic or purely architectural approach would have.

2. High Potential for Impact: This work addresses a critical and timely problem. 3DGS has emerged as a leading representation for scene rendering, but its application in robotics and AR/VR is gated by performance. By demonstrating a clear path to real-time execution on edge platforms, this paper could unlock the widespread adoption of 3DGS for a new class of applications, from on-device photorealistic mapping for AR glasses to more capable autonomous robots. It effectively transforms 3DGS-SLAM from a near-real-time curiosity into a practical engineering solution.

3. Insightful and "Low-Overhead" Redundancy Reduction: A key insight of the paper is that the process of identifying and eliminating redundancy must itself be low-cost. The proposed methods are elegant in this regard. For example, using existing gradients for pruning (Section 4.1, page 5) and reusing keyframe decisions for downsampling (Section 4.2, page 6) avoids the need for expensive, orthogonal analysis steps. Similarly, the hardware's use of inter-iteration similarity for scheduling (Section 5.2, page 8) is a clever way to amortize the cost of workload analysis. This design principle is what makes the proposed speedups practically achievable.

4. Excellent Contextualization and Motivation: The paper does a superb job of positioning itself within the broader landscape. The introduction clearly traces the evolution of SLAM representations, and Table 1 (page 2) provides a concise and effective comparison against related hardware acceleration works (GauSPU, GSArch, etc.), clearly articulating the novelty of RTGS's more comprehensive approach. The detailed profiling results in Section 3 serve as a strong, data-driven motivation for every subsequent design decision.

Weaknesses

As a reviewer focused on synthesis and potential, the weaknesses noted here are less about flaws in the execution and more about the scope and future challenges of the work.

1. Specificity of the Hardware Solution: The proposed hardware architecture is tightly coupled to the specific pipeline structure of 3DGS (e.g., projection, sorting, alpha blending). While the authors suggest in the conclusion (Section 8, page 12) that the co-design could be applied to other differentiable renderers like NvDiffRec or Pulsar, this claim is not substantiated. It is unclear how concepts like the R&B Buffer or the WSU's pairwise pixel scheduling would map to fundamentally different rendering paradigms, such as volumetric rendering in NeRF, which have different memory access patterns and pipeline bottlenecks.

2. Focus on Pipeline Acceleration, Not SLAM Fundamentals: This is a choice of scope, not a flaw, but it is important to recognize. The work brilliantly accelerates the per-frame processing of existing 3DGS-SLAM algorithms. However, it does not engage with or propose improvements for other fundamental SLAM challenges like robust loop closure, long-term map management, or relocalization in the context of 3D Gaussian representations. The overall system's robustness and accuracy are still fundamentally limited by the base algorithm it accelerates.

Questions to Address In Rebuttal

1. The claim that the RTGS co-design techniques are applicable to other differentiable rendering systems is intriguing. Could the authors elaborate on this? For example, how would the concept of the R&B Buffer, which reuses data between forward rendering and backpropagation, be adapted for a NeRF-style volumetric renderer where the "forward pass" involves ray marching and querying an MLP?

2. A full SLAM system requires more than just fast tracking and mapping; it needs robust long-term operation. Does the adaptive Gaussian pruning or dynamic resolution scaling for non-keyframes introduce any potential risks for long-term map consistency or the ability to perform successful loop closures later in a trajectory? For instance, could aggressive pruning remove Gaussians that, while unimportant for the current frame, are critical for recognizing a previously visited location?

3. With the core rendering and backpropagation pipeline so effectively accelerated by the RTGS plug-in, what do the authors foresee as the next major system-level bottleneck? Will the workload shift to the "classic" GPU components responsible for preprocessing and sorting, or will the system become limited by off-chip memory bandwidth despite the on-chip optimizations?

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Review Form

Reviewer: The Innovator (Novelty Specialist)

Summary

The authors present RTGS, an algorithm-hardware co-design framework intended to accelerate 3D Gaussian Splatting-based SLAM (3DGS-SLAM) to real-time performance on edge devices. The core thesis is that significant performance gains can be unlocked by systematically identifying and reducing redundancies at multiple levels of the SLAM pipeline, from individual Gaussians and pixels to entire frames and iterations.

The claimed novelty is not a single, groundbreaking algorithm or architectural principle. Rather, it lies in the comprehensive synthesis and co-design of several known optimization techniques, specifically tailored to the unique workload characteristics of 3DGS-SLAM. The authors propose algorithmic modifications (adaptive pruning, dynamic downsampling) and a corresponding hardware plug-in with specialized units (WSU, R&B Buffer, GMU) to implement these optimizations with minimal overhead. While the resulting system demonstrates a significant performance leap, an analysis of the individual components reveals that most are adaptations of well-established concepts from adjacent fields.

Strengths

The primary strength and most novel aspect of this work is its holistic, multi-level co-design approach. The authors correctly identify that a single-point optimization is insufficient. The main contributions that can be considered novel in their application context are:

1. Exploitation of Inter-Iteration Similarity: The insight that workload distributions are highly similar across optimization iterations within the same frame (Observation 6, page 5) is a key enabler. Using this temporal coherence to inform the pixel-level pairwise scheduling in the Workload Scheduling Unit (WSU) is a clever, application-specific optimization that reduces scheduling overhead.
2. Overhead-Aware Optimizations: The paper demonstrates a keen awareness of the cost of optimization itself. For instance, the adaptive Gaussian pruning reuses gradients already computed for backpropagation (Section 4.1, page 5), and the R&B Buffer reuses intermediate rendering values for the backward pass (Section 5.2, page 7). This focus on minimizing the meta-cost of redundancy reduction is a noteworthy engineering contribution.

Weaknesses

My primary concern is the conceptual novelty of the individual techniques employed. While the authors have engineered a complex and effective system, the foundational ideas are largely derivative of prior art.

1. Gaussian Pruning: The use of gradient magnitudes to determine importance is a standard technique in neural network pruning. The paper’s contribution (Section 4.1, page 5) is the application of this principle to 3D Gaussians within a SLAM loop, combined with a progressive masking strategy. While effective, this is an application of a known method, not the invention of a new one. The paper itself cites Taming 3DGS [29] as a prior method for pruning, with the main delta being that RTGS's method is better suited for the low-iteration count of SLAM.
2. Dynamic Downsampling: Adaptive resolution based on frame content or importance is a classic technique in real-time graphics and video compression. The idea of treating keyframes and non-keyframes differently is the cornerstone of modern SLAM. The proposed heuristic of progressively scaling resolution based on distance from the last keyframe (Section 4.2, page 6) is a specific implementation choice, not a new paradigm.
3. Workload Balancing: The problem of workload imbalance in rendering is decades old. Tile-based rendering and various dynamic scheduling strategies exist to combat this. The authors themselves acknowledge in Table 1 (page 2) that GauSPU [49] and MetaSapiens [23] address this at the tile level. The contribution of RTGS is to move this to a finer, pixel-level granularity, which is an incremental, albeit logical, refinement.
4. Hardware Specialization: The R&B Buffer is a form of specialized cache or memoization for the forward/backward pass, a common pattern in deep learning accelerators. Similarly, the Gradient Merging Unit (GMU) is a hardware implementation of a reduction tree to mitigate atomic operation hazards, a problem and solution-pattern seen in prior work like DISTWAR [5] and SIGMA [36], which the authors cite. The novelty is the direct mapping of these architectural patterns to the 3DGS workload, not the patterns themselves.

In summary, the work is a strong piece of engineering that combines existing ideas into a new context. However, it lacks a central, fundamentally new concept. The novelty is in the sum of its parts, not in any individual part.

Questions to Address In Rebuttal

The authors should clarify the "delta" between their work and prior art with greater precision.

1. The WSU's scheduling is guided by the previous iteration. Given the high-speed, parallel nature of GPUs, how does this differ conceptually from existing dynamic scheduling and work-stealing techniques that react to queue lengths and other real-time load metrics? Is the benefit purely from avoiding the overhead of on-the-fly analysis, and if so, how does this hold up if scene or camera motion becomes highly erratic?
2. The paper argues that the combination of these techniques requires a hardware-algorithm co-design. Could the core algorithmic ideas (gradient reuse for pruning, progressive downsampling) be implemented efficiently in software (e.g., via CUDA) on a future-generation GPU with more flexible atomic operations or scheduling primitives? Please justify why a dedicated hardware plug-in is essential, rather than just beneficial.
3. Regarding the GMU, the paper argues that the sparsity pattern in SLAM is different from other workloads. Can you quantify this difference and explain precisely how the proposed reduction tree architecture is uniquely suited to this pattern in a way that prior work on sparse aggregation (e.g., SIGMA [36]) is not?